In one example, a system comprises a programmable logic block comprising programmable logic and a configuration block to store and provide configuration data to the programmable logic, the configuration block comprising a flash memory array to store the configuration data, and the flash memory array comprising an array of split-gate flash memory cells.
In one example, a system comprises a programmable logic block comprising programmable logic and a configuration block to store and provide configuration data to the programmable logic, the configuration block comprising a flash memory array to store the configuration data, and the flash memory array comprising an array of split-gate flash memory cells.
G11C 16/34 - Determination of programming status, e.g. threshold voltage, overprogramming or underprogramming, retention
G11C 11/56 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using storage elements with more than two stable states represented by steps, e.g. of voltage, current, phase, frequency
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
3.
PROGRAMMING OF A SELECTED NON-VOLATILE MEMORY CELL BY CHANGING PROGRAMMING PULSE CHARACTERISTICS
In one example, a method comprises applying a first programming pulse to a terminal of a selected non-volatile memory cell; and applying a second programming pulse to the terminal of the selected non-volatile memory cell, wherein a magnitude of a voltage the second programming pulse is equal to or lower than a magnitude of a voltage of the first programming pulse; wherein the selected non-volatile memory cell is programmed to a target value by the first programming pulse and the second programming pulse.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
The disclosed memory device (60) includes a first conductive contact (36) extending through a first insulation material (32), a second conductive material (38a) on the first conductive contact, a resistive switching dielectric material (42a) on the second conductive material, a first conductive material (44a) on the resistive switching dielectric material, and an insulation layer (46) disposed over the first conductive material, the resistive switching dielectric material, and the second conductive material, wherein the resistive switching dielectric material and the upper conductive material are laterally displaced from the first conductive contact. Fabrication involves at least two annealing processes, one after forming the resistive switching dielectric material and another after forming the upper conductive material and/or insulation layer.
A method of forming a semiconductor device by providing a substrate having bulk silicon (10a), an insulation layer (10b) over the bulk silicon, and a silicon layer (10c) over the insulation layer. The silicon and insulation layers are removed from first (16) and second areas (18), while maintained in a third area (20). A memory cell (24) is formed in the first area having a floating gate (32) over a first portion of a memory cell channel region (30) and a control gate (34) over a second portion of the memory cell channel region. A transistor device (40) is formed in the second area having a transistor gate (48) over a transistor channel region (46). A logic device (60) is formed in the third area having a logic device gate (68) over a logic device channel region (66). The memory cell channel region and the transistor channel region are disposed in the bulk silicon. The logic device channel region is disposed in the silicon layer.
H10B 41/40 - Electrically erasable-and-programmable ROM [EEPROM] devices comprising floating gates characterised by the peripheral circuit region
H01L 27/12 - Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including integrated passive circuit elements with at least one potential-jump barrier or surface barrier the substrate being other than a semiconductor body, e.g. an insulating body
H10B 41/49 - Simultaneous manufacture of periphery and memory cells comprising different types of peripheral transistor
H01L 21/28 - Manufacture of electrodes on semiconductor bodies using processes or apparatus not provided for in groups
H01L 29/423 - Electrodes characterised by their shape, relative sizes or dispositions not carrying the current to be rectified, amplified or switched
6.
METHOD OF MAKING MEMORY CELLS, TRANSISTOR DEVICES AND LOGIC DEVICES ON SILICON-ON-INSULATOR SUBSTRATE
A method of forming a semiconductor device by providing a substrate having bulk silicon, an insulation layer over the bulk silicon, and a silicon layer over the insulation layer. The silicon and insulation layers are removed from first and second areas, while maintained in a third area. A memory cell is formed in the first area having a floating gate over a first portion of a memory cell channel region and a control gate over a second portion of the memory cell channel region. A transistor device is formed in the second area having a transistor gate over a transistor channel region. A logic device is formed in the third area having a logic device gate over a logic device channel region. The memory cell channel region and the transistor channel region are disposed in the bulk silicon. The logic device channel region is disposed in the silicon layer.
H10B 41/27 - Electrically erasable-and-programmable ROM [EEPROM] devices comprising floating gates characterised by three-dimensional arrangements, e.g. with cells on different height levels with source and drain on different levels, e.g. with sloping channels the channels comprising vertical portions, e.g. U-shaped channels
H01L 29/423 - Electrodes characterised by their shape, relative sizes or dispositions not carrying the current to be rectified, amplified or switched
A method of programming a memory device comprising control gate lines, erase gate lines, select gate lines, source lines and bit lines connected to rows and columns of memory cells, the method comprising performing an erase operation that includes applying a first voltage to one of the erase gate lines, performing a pre-program operation that includes electrically connecting the one of the erase gate lines to a pair of the control gate lines to use positive charge on the one of the erase gate lines from the erase operation to pre-charge the pair of the control gate lines, and performing a program operation that includes applying a second voltage to the one of the erase gate lines and the pair of the control gate lines.
A method of programming memory cells that includes reading the memory cells to determine respective read currents for the memory cells. Respective ones of the memory cells are assigned to one of a plurality of groups of the memory cells, wherein respective ones of the plurality of groups of the memory cells are associated with a different range of read currents, such that the determined read current for a respective one of the memory cells is within the range of read currents for the group of the memory cells to which the memory cell is assigned. The memory cells are programmed using program currents that vary for respective ones of the memory cells depending upon the group of the memory cells to which the memory cell is assigned.
G11C 11/56 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using storage elements with more than two stable states represented by steps, e.g. of voltage, current, phase, frequency
G11C 16/04 - Erasable programmable read-only memories electrically programmable using variable threshold transistors, e.g. FAMOS
G11C 16/26 - Sensing or reading circuitsData output circuits
9.
ACCELERATED PROGRAMMING OF FOUR-GATE, SPLIT-GATE FLASH MEMORY CELLS
A method of programming a memory device comprising control gate lines, erase gate lines, select gate lines, source lines and bit lines connected to rows and columns of memory cells, the method comprising performing an erase operation that includes applying a first voltage to one of the erase gate lines, performing a pre-program operation that includes electrically connecting the one of the erase gate lines to a pair of the control gate lines to use positive charge on the one of the erase gate lines from the erase operation to pre-charge the pair of the control gate lines, and performing a program operation that includes applying a second voltage to the one of the erase gate lines and the pair of the control gate lines.
G11C 11/56 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using storage elements with more than two stable states represented by steps, e.g. of voltage, current, phase, frequency
10.
LOW VOLTAGE RESISTIVE RANDOM ACCESS MEMORY (RRAM) CELLS AND METHOD OF FORMATION
A memory device, and method of formation, that includes a first insulation material disposed over a semiconductor substrate. A first conductive contact extends through the first insulation material. A second block of conductive material is disposed on the first insulation material and on, and in electrical contact with, the first conductive contact. A block of resistive switching dielectric material is disposed directly on the second block of conductive material. A first block of conductive material is disposed directly on the block of resistive switching dielectric material. The block of resistive switching dielectric material and the first block of conductive material are displaced laterally from the first conductive contact. An insulation layer is disposed over the first block of conductive material, the block of resistive switching dielectric material and the second block of conductive material.
A method of programming memory cells that includes reading the memory cells to determine respective read currents for the memory cells. Respective ones of the memory cells are assigned to one of a plurality of groups of the memory cells, wherein respective ones of the plurality of groups of the memory cells are associated with a different range of read currents, such that the determined read current for a respective one of the memory cells is within the range of read currents for the group of the memory cells to which the memory cell is assigned. The memory cells are programmed using program currents that vary for respective ones of the memory cells depending upon the group of the memory cells to which the memory cell is assigned.
In one example, a system comprises a vector-by-matrix multiplication array comprising an array of non-volatile memory cells arranged in rows and columns; and a sigma-delta analog-to-digital converter to receive a current from a column of the vector-by-matrix multiplication array and to generate a digital output in response to the current.
In one example, an analog neural memory system comprises a vector-by-matrix multiplication array comprising an array of non-volatile memory cells organized into rows and columns, wherein each memory cell comprises a bit line terminal, a control gate terminal, and a word line terminal; a plurality of bit lines, wherein each of the plurality of bit lines is coupled to the bit line terminals of a column of memory cells; a plurality of control gate lines, wherein each of the plurality of control gate lines is coupled to the control gate terminals of a row of memory cells; and a plurality of word lines, wherein each of the plurality of word lines is coupled to the word line terminals of a row of memory cells; wherein the plurality of control gate lines are parallel to the plurality of bit lines and perpendicular to the plurality of word lines.
G11C 11/56 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using storage elements with more than two stable states represented by steps, e.g. of voltage, current, phase, frequency
G06N 3/06 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
G11C 11/16 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using magnetic elements using elements in which the storage effect is based on magnetic spin effect
G11C 11/4074 - Power supply or voltage generation circuits, e.g. bias voltage generators, substrate voltage generators, back-up power, power control circuits
14.
SIGMA-DELTA ANALOG-TO-DIGITAL CONVERTER TO GENERATE DIGITAL OUTPUT FROM VECTOR-BY-MATRIX MULTIPLICATION ARRAY
In one example, a system comprises a vector-by-matrix multiplication array comprising an array of non-volatile memory cells arranged in rows and columns; and a sigma-delta analog-to-digital converter to receive a current from a column of the vector-by-matrix multiplication array and to generate a digital output in response to the current.
H03M 1/46 - Analogue value compared with reference values sequentially only, e.g. successive approximation type with digital/analogue converter for supplying reference values to converter
15.
GROUPING AND ERROR CORRECTION FOR NON-VOLATILE MEMORY CELLS
Numerous examples are disclosed of an improved grouping and error correction system for non-volatile memory cells. In one example, a system comprises a memory array comprising non-volatile memory cells arranged into rows and columns, wherein the array stores a plurality of words, wherein respective words are divided into multiple sub-words and respective non-volatile memory cells in the memory array store digital bits belonging to different sub-words of the plurality of sub-words.
In one example, a system comprises a plurality of charge pump units connected in parallel to receive an input voltage and to generate an output voltage greater than the input voltage; and a pumping controller to provide a pumping signal to a first charge pump unit of the plurality of charge pump units and to provide sequentially delayed versions of the pumping signal to the other charge pump units of the plurality of charge pump units, the pumping controller comprising a plurality of circuit blocks, each of the plurality of circuit blocks comprising a delay circuit and a latch.
H02M 3/07 - Conversion of DC power input into DC power output without intermediate conversion into AC by static converters using resistors or capacitors, e.g. potential divider using capacitors charged and discharged alternately by semiconductor devices with control electrode
In one example, a method comprises determining a logarithmic slope factor for a selected analog non-volatile memory cell in an array of analog non-volatile memory cells while the selected analog non-volatile memory cell is operating in a sub-threshold region; storing the logarithmic slope factor; determining a linear slope factor for the selected analog non-volatile memory cell while the selected analog non-volatile memory cell is operating in a linear region; storing the linear slope factor; and utilizing one or more of the logarithmic slope factor and the linear slope factor when programming the selected analog non-volatile memory cell to a target current.
A method of operating memory cells includes programming memory cells at a first temperature to different program states associated with first read current values confirmed by using nominal read conditions. Modified read conditions are determined such that a second read current for the one memory cells at a second temperature is approximately equal to the first read current value for the one memory cell. A read operation is performed on the memory cells at the second temperature using the modified read conditions to determine respective third read current values. Error read current values are determined as respective differences between the first and third read current values. Upper and lower program states are assigned to respective desired program states, with read currents that correspond approximately to respective determined error read current values, and are separated approximately by a respective target read current value associated with the respective desired program state.
G11C 7/04 - Arrangements for writing information into, or reading information out from, a digital store with means for avoiding disturbances due to temperature effects
G11C 29/02 - Detection or location of defective auxiliary circuits, e.g. defective refresh counters
G11C 11/56 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using storage elements with more than two stable states represented by steps, e.g. of voltage, current, phase, frequency
G11C 16/04 - Erasable programmable read-only memories electrically programmable using variable threshold transistors, e.g. FAMOS
In one example, a system comprises a vector-by-matrix multiplication array comprising non-volatile memory cells arranged into rows and columns, a first set of columns storing W+ weights and a second set of columns storing W- weights; and an output circuit to receive a first current from a respective column in the first set of columns and a second current from a respective column in the second set of columns and to generate a first voltage and a second voltage, the output circuit comprising a first current-to-voltage converter comprising a first integration capacitor to provide the first voltage equal to an initial voltage minus a first discharge value due to the first current, and a second current-to-voltage converter comprising a second integration capacitor to provide the second voltage equal to the initial voltage minus a second discharge value due to the second current.
G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
Numerous examples are disclosed of input blocks for an array of non-volatile memory cells and associated methods. In one example, a system comprises a vector-by-matrix multiplication array comprising non-volatile memory cells arranged into rows and columns; and an input block comprising a plurality of row circuits and a global digital-to-analog converter generator to generate 2m different analog voltages, where m is an integer; wherein the row circuits in the plurality of row circuits respectively apply one of the 2m different analog voltages to an associated row in the array.
In one example, a system comprises a vector-by-matrix multiplication array comprising non-volatile memory cells arranged into rows and columns, a first set of columns storing W+ weights and a second set of columns storing W− weights; and an output circuit to receive a first current from a respective column in the first set of columns and a second current from a respective column in the second set of columns and to generate a first voltage and a second voltage, the output circuit comprising a first current-to-voltage converter comprising a first integration capacitor to provide the first voltage equal to an initial voltage minus a first discharge value due to the first current, and a second current-to-voltage converter comprising a second integration capacitor to provide the second voltage equal to the initial voltage minus a second discharge value due to the second current.
Numerous examples are disclosed of input blocks for an array of non-volatile memory cells and associated methods. In one example, a system comprises a vector-by-matrix multiplication array comprising non-volatile memory cells arranged into rows and columns; and an input block comprising a plurality of row circuits and a global digital-to-analog converter generator to generate 2mdifferent analog voltages, where m is an integer; wherein the row circuits in the plurality of row circuits respectively apply one of the 2m different analog voltages to an associated row in the array.
H03M 1/46 - Analogue value compared with reference values sequentially only, e.g. successive approximation type with digital/analogue converter for supplying reference values to converter
23.
Memory Device Formed On Silicon-On-Insulator Substrate, And Method Of Making Same
A memory device includes a SOI substrate comprising bulk silicon, an insulation layer vertically over the bulk silicon, and a silicon layer vertically over the insulation layer. A memory cell includes source and drain regions formed in the bulk silicon with a channel region of the bulk silicon extending therebetween, and a floating gate which includes a first portion of the silicon layer disposed vertically over and insulated from a first portion of the channel region by the insulation layer. The first portion of the silicon layer is epitaxially thickened or a layer of polysilicon is formed on the first portion of the silicon layer. A select gate is disposed vertically over and insulated from a second portion of the channel region. A control gate is disposed vertically over and insulated from the floating gate. An erase gate is disposed vertically over and insulated from the source region.
A memory device includes a SOI substrate comprising bulk silicon (12), an insulation layer (14) vertically over the bulk silicon, and a silicon layer (16) vertically over the insulation layer. A memory cell (20) includes source (30) and drain (32) regions formed in the bulk silicon with a channel region (34) of the bulk silicon extending therebetween, and a floating gate (36) which includes a first portion of the silicon layer disposed vertically over and insulated from a first portion of the channel region by the insulation layer. The first portion of the silicon layer is epitaxially thickened or a layer of polysilicon (62) is formed on the first portion of the silicon layer. A select gate (38) is disposed vertically over and insulated from a second portion of the channel region. A control gate (40) is disposed vertically over and insulated from the floating gate. An erase gate (42) is disposed vertically over and insulated from the source region.
H01L 21/28 - Manufacture of electrodes on semiconductor bodies using processes or apparatus not provided for in groups
H01L 29/423 - Electrodes characterised by their shape, relative sizes or dispositions not carrying the current to be rectified, amplified or switched
H10B 41/30 - Electrically erasable-and-programmable ROM [EEPROM] devices comprising floating gates characterised by the memory core region
H10B 41/43 - Simultaneous manufacture of periphery and memory cells comprising only one type of peripheral transistor
H01L 27/12 - Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including integrated passive circuit elements with at least one potential-jump barrier or surface barrier the substrate being other than a semiconductor body, e.g. an insulating body
In one example, a circuit for comparing current drawn by a selected memory cell for a vector-matrix-multiplier with current drawn by a reference matrix comprises a first circuit comprising a first PMOS transistor coupled to a first NMOS transistor coupled to the selected memory cell; and a second circuit comprising a second PMOS transistor coupled to a second NMOS transistor coupled to the reference matrix; wherein a node between the second PMOS transistor and the second NMOS transistor outputs a current indicative of a value stored in the selected memory cell.
Numerous examples are disclosed of a masking circuit for inputs and outputs in a neural network array. In one example, a system comprises a neural network array comprising a plurality of non-volatile memory cells arranged into rows and columns; and row circuits for respective rows in the neural network array, the row circuits comprising a masking circuit to prevent an application of a sparse input to one or more rows in the array when a condition is satisfied.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
G11C 8/08 - Word line control circuits, e.g. drivers, boosters, pull-up circuits, pull-down circuits, precharging circuits, for word lines
27.
ANALOG COMPUTATION-IN-MEMORY ENGINE AND DIGITAL COMPUTATION-IN-MEMORY ENGINE TO PERFORM OPERATIONS IN A NEURAL NETWORK
In one example disclosed herein, a system comprises an analog computation-in-memory engine to perform operations in a first layer in a neural network and a digital computation-in-memory engine to perform operations in a second layer different than the first layer in the neural network. The system optionally comprises a dynamic weight engine to perform operations in a third layer different than the first layer and the second layer in the neural network.
G11C 11/00 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor
G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G11C 16/04 - Erasable programmable read-only memories electrically programmable using variable threshold transistors, e.g. FAMOS
G11C 27/00 - Electric analogue stores, e.g. for storing instantaneous values
G11C 11/413 - Auxiliary circuits, e.g. for addressing, decoding, driving, writing, sensing, timing or power reduction
G11C 11/412 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using electric elements using semiconductor devices using transistors forming cells with positive feedback, i.e. cells not needing refreshing or charge regeneration, e.g. bistable multivibrator or Schmitt trigger using field-effect transistors only
28.
ROW DECODER AND ROW ADDRESS SCHEME IN A MEMORY SYSTEM
Numerous examples are disclosed of a row address decoding scheme. In one example, a memory system comprises m banks of non-volatile memory cells, the m banks respectively comprising n or fewer sectors and the sectors respectively comprising p rows, and a row decoder to receive a row address comprising r bits and to identify (i) a row using the least significant t bits in the r bits, (ii) a bank using the next u least significant bits, and (iii) a sector using the next v least significant bits, where m≤2u, n≤2v, and p≤2t.
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
G11C 7/12 - Bit line control circuits, e.g. drivers, boosters, pull-up circuits, pull-down circuits, precharging circuits, equalising circuits, for bit lines
29.
ANALOG COMPUTATION-IN-MEMORY ENGINE AND DIGITAL COMPUTATION-IN-MEMORY ENGINE TO PERFORM OPERATIONS IN A NEURAL NETWORK
In one example disclosed herein, a system comprises an analog computation-in-memory engine to perform operations in a first layer in a neural network and a digital computation-in-memory engine to perform operations in a second layer different than the first layer in the neural network. The system optionally comprises a dynamic weight engine to perform operations in a third layer different than the first layer and the second layer in the neural network.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
30.
ROW DECODER AND ROW ADDRESS SCHEME IN A MEMORY SYSTEM
Numerous examples are disclosed of a masking circuit for inputs and outputs in a neural network array. In one example, a system comprises a neural network array comprising a plurality of non-volatile memory cells arranged into rows and columns; and row circuits for respective rows in the neural network array, the row circuits comprising a masking circuit to prevent an application of a sparse input to one or more rows in the array when a condition is satisfied.
Numerous embodiments are disclosed for splitting a physical array into multiple arrays for separate vector-by-matrix multiplication (VMM) operations. In one example, a system comprises an array of non-volatile memory cells arranged into rows and columns; and a plurality of sets of output lines, where each column contains a set of output lines; wherein each row is coupled to only one output line in the set of output lines for each column.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G11C 16/04 - Erasable programmable read-only memories electrically programmable using variable threshold transistors, e.g. FAMOS
33.
ERASING OF A WORD OR A PAGE OF NON-VOLATILE MEMORY CELLS IN AN ANALOG NEURAL MEMORY SYSTEM
In one example, a method comprises erasing at the same time a word of non-volatile memory cells in an array of non-volatile memory cells arranged into rows and columns, each non-volatile memory cell comprising a word line terminal, a bit line terminal, and an erase gate terminal, by turning on an erase gate enable transistor coupled to erase gate terminals of the word of non-volatile memory cells.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
A system comprises a vector-by-matrix multiplication array of non-volatile memory cells arranged into rows and columns, the array comprising a first bit line coupled to a first column of non-volatile memory cells and a second bit line coupled to a second column of non-volatile memory cells; and an output block coupled to the array, the output block comprising: a current-to-voltage converter to convert a first current on the first bit line into a first voltage and to convert a second current on the second bit line into a second voltage; and an analog-to-digital converter to convert one or more of the first voltage and the second voltage into a set of output bits.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G11C 11/56 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using storage elements with more than two stable states represented by steps, e.g. of voltage, current, phase, frequency
G11C 16/26 - Sensing or reading circuitsData output circuits
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
G11C 7/16 - Storage of analogue signals in digital stores using an arrangement comprising analogue/digital [A/D] converters, digital memories and digital/analogue [D/A] converters
G11C 16/04 - Erasable programmable read-only memories electrically programmable using variable threshold transistors, e.g. FAMOS
35.
Neural network classifier using array of three-gate non-volatile memory cells
A neural network device with synapses having memory cells each having a floating gate and a first gate over first and second portions of a channel region disposed between source and drain regions, and a second gate over the floating gate or the source region. First lines each electrically connect the first gates in one of the memory cell rows, second lines each electrically connect the second gates in one of the memory cell rows, third lines each electrically connect the source regions in one of the memory cell rows, and fourth lines each electrically connect the drain regions in one of the memory cell columns. The synapses receive a first plurality of inputs as electrical voltages on the fourth lines, and provide a first plurality of outputs as electrical currents on the third lines.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
In one example, a system comprises an array of non-volatile memory cells arranged into rows and columns, the array comprising a first bit line coupled to a first column of non-volatile memory cells and a second bit line coupled to a second column of non-volatile memory cells; and an output block coupled to the array, the output block comprising: a current-to-voltage converter to convert a first current on the first bit line into a first voltage and to convert a second current on the second bit line into a second voltage; and an analog-to-digital converter to convert one or more of the first voltage and the second voltage into a set of output bits.
G11C 7/16 - Storage of analogue signals in digital stores using an arrangement comprising analogue/digital [A/D] converters, digital memories and digital/analogue [D/A] converters
G11C 7/12 - Bit line control circuits, e.g. drivers, boosters, pull-up circuits, pull-down circuits, precharging circuits, equalising circuits, for bit lines
G11C 7/14 - Dummy cell managementSense reference voltage generators
A semiconductor device includes a semiconductor substrate, a first module of circuitry formed on the semiconductor substrate, a second module of circuitry formed on the semiconductor substrate, and a communication ring that encircles the first module of circuitry. The communication ring includes an insulation material disposed over the semiconductor substrate, a plurality of electrical connectors disposed over the semiconductor substrate and extending across a width of the communication ring, and a conductive diffusion in the semiconductor substrate that encircles the first module of circuitry.
A neural network device with synapses having memory cells each having a floating gate and a first gate over first and second portions of a channel region between source and drain regions, and a second gate over the floating gate or the source region. First lines each electrically connect the first gates in one of the memory cell rows, second lines each electrically connect the second gates in one of the memory cell rows, third lines each electrically connect the source regions in one of the memory cell rows, fourth lines each electrically connect the drain regions in one of the memory cell columns, and a plurality of transistors each electrically connected in series with one of the fourth lines. The synapses receive a first plurality of inputs as electrical voltages on gates of the transistors, and provide a first plurality of outputs as electrical currents on the third lines.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
A semiconductor device includes a semiconductor substrate, a first module of circuitry formed on the semiconductor substrate, a second module of circuitry formed on the semiconductor substrate, and a communication ring that encircles the first module of circuitry. The communication ring includes an insulation material disposed over the semiconductor substrate, a plurality of electrical connectors disposed over the semiconductor substrate and extending across a width of the communication ring, and a conductive diffusion in the semiconductor substrate that encircles the first module of circuitry.
H01L 27/02 - Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including integrated passive circuit elements with at least one potential-jump barrier or surface barrier
H01L 21/38 - Diffusion of impurity materials, e.g. doping materials, electrode materials, into, or out of, a semiconductor body, or between semiconductor regions
H01L 23/58 - Structural electrical arrangements for semiconductor devices not otherwise provided for
40.
Neural network classifier using array of three-gate non-volatile memory cells
A neural network device with synapses having memory cells each having a floating gate and a first gate over first and second portions of a channel region disposed between source and drain regions, and a second gate over the floating gate or the source region. First lines each electrically connect the first gates in one of the memory cell columns, second lines each electrically connect the second gates in one of the memory cell rows, third lines each electrically connect the source regions in one of the memory cell rows, and fourth lines each electrically connect the drain regions in one of the memory cell columns. The synapses receive a first plurality of inputs as electrical voltages on the first or fourth lines, and provide a first plurality of outputs as electrical currents on the third lines.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
In one example, a system comprises a current-to-voltage converter to generate differential voltages from differential currents comprising a first current and a second current, the current-to-voltage converter comprising: a first bitline to provide the first current; a second bitline to provide the second current; a first regulator to apply a first voltage to the first bitline; a second regulator to apply a second voltage to the second bitline; a regulating circuit comprising a first input terminal, a second input terminal, a first output terminal, and a second output terminal, the first output terminal and the second output terminal providing the differential voltages; and a common mode circuit.
A memory cell array having rows and columns of memory cells with respective ones of the memory cells including spaced apart source and drain regions formed in a semiconductor substrate with a channel region extending there between, a floating gate over a first portion of the channel region, a select gate over a second portion of the channel region, and an erase gate over the source region. A strap region is disposed between first and second pluralities of the columns. For one memory cell row, a dummy floating gate is disposed in the strap region, an erase gate line electrically connects together the erase gates of the memory cells in the one row and in the first plurality of columns, wherein the erase gate line is aligned with the dummy floating gate with a row direction gap between the erase gate line and the dummy floating gate.
In one example, a system comprises a plurality of charge pump units connected in parallel to receive an input voltage and to generate an output voltage greater than the input voltage and a pumping controller to provide a pumping signal to a first charge pump unit of the plurality of charge pump units and to provide sequentially delayed versions of the pumping signal to the other charge pump units of the plurality of charge pump units.
H02M 3/07 - Conversion of DC power input into DC power output without intermediate conversion into AC by static converters using resistors or capacitors, e.g. potential divider using capacitors charged and discharged alternately by semiconductor devices with control electrode
Numerous examples are disclosed of multiplexors coupled to rows in a neural network array. In one example, a system comprises a neural network array of non-volatile memory cells comprising i rows, where i is a multiple of 2; j row registers, where j < i; j digital-to-analog converters to convert j sets of digital data received from the j row registers into j analog signals; and j multiplexors to route the j analog signals to a subset of the i rows in response to a control signal.
In one example, a system comprises a current-to-voltage converter to generate differential voltages from differential currents comprising a first current and a second current, the current-to-voltage converter comprising: a first bitline to provide the first current; a second bitline to provide the second current; a first regulator to apply a first voltage to the first bitline; a second regulator to apply a second voltage to the second bitline; a regulating circuit comprising a first input terminal, a second input terminal, a first output terminal, and a second output terminal, the first output terminal and the second output terminal providing the differential voltages; and a common mode circuit.
G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G11C 16/34 - Determination of programming status, e.g. threshold voltage, overprogramming or underprogramming, retention
46.
Pumping controller for a plurality of charge pump units
In one example, a system comprises a plurality of charge pump units connected in parallel to receive an input voltage and to generate an output voltage greater than the input voltage and a pumping controller to provide a pumping signal to a first charge pump unit of the plurality of charge pump units and to provide sequentially delayed versions of the pumping signal to the other charge pump units of the plurality of charge pump units.
H02M 3/07 - Conversion of DC power input into DC power output without intermediate conversion into AC by static converters using resistors or capacitors, e.g. potential divider using capacitors charged and discharged alternately by semiconductor devices with control electrode
47.
REDUNDANCY FOR AN ARRAY OF NON-VOLATILE MEMORY CELLS USING TAG REGISTERS AND FOR A REDUNDANT ARRAY USING TAG REGISTERS
Numerous examples are disclosed of systems and methods to implement redundancy. In one example, a system comprises an array of non-volatile memory cells; a redundant array of non-volatile memory cells; and an input block coupled to respective rows in the array and respective rows in the redundant array and comprising row tag registers and redundant row tag registers.
Numerous examples are disclosed of systems and methods to implement redundancy. In one example, a system comprises an array of non-volatile memory cells; a redundant array of non-volatile memory cells; and an input block coupled to respective rows in the array and respective rows in the redundant array and comprising row tag registers and redundant row tag registers.
A memory cell array having rows and columns of memory cells with respective ones of the memory cells including spaced apart source and drain regions formed in a semiconductor substrate with a channel region extending there between, a floating gate over a first portion of the channel region, a select gate over a second portion of the channel region, and an erase gate over the source region. A strap region is disposed between first and second pluralities of the columns. For one memory cell row, a dummy floating gate is disposed in the strap region, an erase gate line electrically connects together the erase gates of the memory cells in the one row and in the first plurality of columns, wherein the erase gate line is aligned with the dummy floating gate with a row direction gap between the erase gate line and the dummy floating gate.
G11C 5/06 - Arrangements for interconnecting storage elements electrically, e.g. by wiring
G11C 16/16 - Circuits for erasing electrically, e.g. erase voltage switching circuits for erasing blocks, e.g. arrays, words, groups
H01L 23/48 - Arrangements for conducting electric current to or from the solid state body in operation, e.g. leads or terminal arrangements
H01L 29/423 - Electrodes characterised by their shape, relative sizes or dispositions not carrying the current to be rectified, amplified or switched
H10B 41/10 - Electrically erasable-and-programmable ROM [EEPROM] devices comprising floating gates characterised by the top-view layout
H10B 41/27 - Electrically erasable-and-programmable ROM [EEPROM] devices comprising floating gates characterised by three-dimensional arrangements, e.g. with cells on different height levels with source and drain on different levels, e.g. with sloping channels the channels comprising vertical portions, e.g. U-shaped channels
Numerous examples are disclosed of multiplexors coupled to rows in a neural network array. In one example, a system comprises a neural network array of non-volatile memory cells comprising i rows, where i is a multiple of 2; j row registers, where j
Numerous embodiments of an array of non-volatile memory cells are disclosed herein. In one example, a system comprises an array of non-volatile memory cells arranged into rows and columns; wherein in a first mode, the array stores digital data; and wherein in a second mode, the array stores analog data.
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
G11C 7/16 - Storage of analogue signals in digital stores using an arrangement comprising analogue/digital [A/D] converters, digital memories and digital/analogue [D/A] converters
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G11C 27/00 - Electric analogue stores, e.g. for storing instantaneous values
Examples of improved charge pumps are disclosed. In one example, a system comprises a first charge path comprising a first stage to boost an input voltage and a second stage to boost a voltage received from the first stage of the first charge path; and a second charge path comprising a first stage to boost an input voltage and a second stage to boost a voltage received from the first stage of the second charge path; wherein an output of the second stage of the first charge path is coupled to the first stage of the second charge path and an output of the second stage of the second charge path is coupled to the first stage of the first charge path.
Numerous examples are disclosed of an improved grouping and error correction system for non-volatile memory cells. In one example, a system comprises a memory array comprising non-volatile memory cells arranged into rows and columns, wherein a non-volatile memory cell of the memory array stores a first bit of a first data grouping and a second bit of a second data grouping, and wherein the first grouping is backed by a first ECC block and the second grouping is backed by a second ECC block.
Numerous examples are disclosed of an improved grouping and error correction system for non-volatile memory cells. In one example, a system comprises a memory array comprising non-volatile memory cells arranged into rows and columns, wherein a non-volatile memory cell of the memory array stores a first bit of a first data grouping and a second bit of a second data grouping, and wherein the first grouping is backed by a first ECC block and the second grouping is backed by a second ECC block.
Examples of improved charge pumps are disclosed. In one example, a system comprises a first charge path comprising a first stage to boost an input voltage and a second stage to boost a voltage received from the first stage of the first charge path; and a second charge path comprising a first stage to boost an input voltage and a second stage to boost a voltage received from the first stage of the second charge path; wherein an output of the second stage of the first charge path is coupled to the first stage of the second charge path and an output of the second stage of the second charge path is coupled to the first stage of the first charge path.
H02M 3/07 - Conversion of DC power input into DC power output without intermediate conversion into AC by static converters using resistors or capacitors, e.g. potential divider using capacitors charged and discharged alternately by semiconductor devices with control electrode
In one example, a non-volatile memory system, comprises an array of non-volatile memory cells arranged in rows and columns, each non-volatile memory cell comprising a source and a drain; a plurality of bit lines, each of the plurality of bit lines coupled to the drain or each non-volatile memory cell in a column of non-volatile memory cells; a source line coupled to the source of each non-volatile memory cell; and an adaptive bias decoder for providing a voltage to an erase gate line of the array during an operation, wherein the adaptive bias decoder adjusts the voltage provided to the erase gate line in response to changes in a voltage of the source line.
G11C 16/08 - Address circuitsDecodersWord-line control circuits
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
In one example, a method comprises applying a first programming pulse to a terminal of a selected non-volatile memory cell; and applying a second programming pulse to the terminal of the selected non-volatile memory cell, wherein a magnitude of a voltage the second programming pulse is equal to or lower than a magnitude of a voltage of the first programming pulse; wherein the selected non-volatile memory cell is programmed to a target value by the first programming pulse and the second programming pulse.
G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
In one example, a system comprises an analog neural memory array comprising a plurality of non-volatile memory cells arranged into rows and columns; and a voltage generator to provide a voltage to one or more rows of the analog neural memory array, the voltage generator comprising a voltage ladder to generate a plurality of voltages according to a logarithmic formula.
Numerous examples are disclosed of programming multiple rows in an array in an artificial neural network as part of a single programming operation. In one example, a method comprises ramping up an output of a high voltage generator to a first voltage level; while maintaining the output of the high voltage generator at the first voltage level, programming a plurality of words of K rows of memory cells in an array of memory cells using the output of the high voltage generator, where K>1; and after the programming, ramping down the output of the high voltage generator to a second voltage level.
G11C 11/56 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using storage elements with more than two stable states represented by steps, e.g. of voltage, current, phase, frequency
G06N 3/04 - Architecture, e.g. interconnection topology
Numerous examples are disclosed of output circuitry and associated methods in an artificial neural network. In one example, a system comprises an array of non-volatile memory cells arranged into rows and columns, an output block to convert current from columns of the array into a first digital output during a first time period and a second digital output during a second time period, a first output register to store the first digital output during the first time period and to output the stored first digital output during the second time period, and a second output register to store the second digital output during the second time period and to output the stored second digital output during a third time period.
G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
G06F 5/01 - Methods or arrangements for data conversion without changing the order or content of the data handled for shifting, e.g. justifying, scaling, normalising
G06F 7/501 - Half or full adders, i.e. basic adder cells for one denomination
Numerous examples are disclosed of input circuitry and associated methods in an artificial neural network. In one example, a system comprises a plurality of address decoders to receive an address and output a plurality of row enabling signals in response to the address; a first plurality of registers to store, sequentially, activation data in response to the plurality of row enabling signals; and a second plurality of registers to store, in parallel, activation data received from the first plurality of registers.
In one example, a non-volatile memory system comprises an array of non-volatile memory cells arranged in rows and columns, each non-volatile memory cell comprising a source and a drain; a plurality of bit lines, each of the plurality of bit lines coupled to the drain of each non-volatile memory cell in a column of non-volatile memory cells; a source line coupled to the source of each non-volatile memory cell; and an adaptive bias decoder for providing a voltage to a word line of the array during an operation, wherein the adaptive bias decoder adjusts the voltage provided to the word line in response to changes in a voltage of the source line.
G11C 16/08 - Address circuitsDecodersWord-line control circuits
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
Numerous examples are disclosed of input circuitry and associated methods in an artificial neural network. In one example, a system comprises a plurality of address decoders to receive an address and output a plurality of row enabling signals in response to the address; a first plurality of registers to store, sequentially, activation data in response to the plurality of row enabling signals; and a second plurality of registers to store, in parallel, activation data received from the first plurality of registers.
G11C 8/18 - Address timing or clocking circuitsAddress control signal generation or management, e.g. for row address strobe [RAS] or column address strobe [CAS] signals
G11C 8/06 - Address interface arrangements, e.g. address buffers
G11C 8/08 - Word line control circuits, e.g. drivers, boosters, pull-up circuits, pull-down circuits, precharging circuits, for word lines
G11C 8/04 - Arrangements for selecting an address in a digital store using a sequential addressing device, e.g. shift register, counter
G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
64.
MULTIPLE ROW PROGRAMMING OPERATION IN ARTIFICIAL NEURAL NETWORK ARRAY
Numerous examples are disclosed of programming multiple rows in an array in an artificial neural network as part of a single programming operation. In one example, a method comprises ramping up an output of a high voltage generator to a first voltage level; while maintaining the output of the high voltage generator at the first voltage level, programming a plurality of words of K rows of memory cells in an array of memory cells using the output of the high voltage generator, where K > 1; and after the programming, ramping down the output of the high voltage generator to a second voltage level.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
Numerous examples are disclosed of output circuitry and associated methods in an artificial neural network. In one example, a system comprises an array of non-volatile memory cells arranged into rows and columns, an output block to convert current from columns of the array into a first digital output during a first time period and a second digital output during a second time period, a first output register to store the first digital output during the first time period and to output the stored first digital output during the second time period, and a second output register to store the second digital output during the second time period and to output the stored second digital output during a third time period.
G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
Numerous examples are disclosed of verification circuitry and associated methods in an artificial neural network. In one example, a system comprises a vector-by-matrix multiplication array comprising a plurality of non-volatile memory cells arranged in rows and columns, the non-volatile memory cells respectively capable of storing one of N possible levels corresponding to one of N possible currents, and a plurality of output blocks to receive current from respective columns of the vector-by-matrix multiplication array and generate voltages during a verify operation of the vector-by-matrix multiplication and generate digital outputs during a read operation of the vector-by-matrix multiplication.
G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
In one example, a neural network device comprises a first plurality of synapses configured to receive a first plurality of inputs and to generate therefrom a first plurality of outputs, wherein the first plurality of synapses comprises a plurality of memory cells, each of the plurality of memory cells configured to store a weight value corresponding to a number of electrons on its floating gate and the plurality of memory cells are configured to generate the first plurality of outputs based upon the first plurality of inputs and the stored weight values.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
69.
WORD LINE DRIVER FOR VECTOR-BY-MATRIX MULTIPLICATION ARRAY
In one example, a system comprises a vector-by-matrix multiplication array comprising non-volatile memory cells organized into rows and columns; a plurality of word lines coupled respectively to rows of the vector-by-matrix multiplication array; and a word line driver coupled to the plurality of word lines, the word line driver comprising a plurality of select transistors coupled to a common control line and the plurality of word lines, and a plurality of bias transistors coupled to the plurality of select transistors and capable of providing a bias voltage to a single select transistor in the plurality of select transistors or to all of plurality of select transistors in response to control signals.
In one example, a system comprises an array comprising selected memory cells; an input block configured to apply, to each selected memory cell, a series of input signals to a terminal of the selected memory cell in response to a series of input bits; and an output block for generating an output of the selected memory cells, the output block comprising an analog-to-digital converter to convert current from the selected memory cells into a digital value, a shifter, an adder, and a register; wherein the shifter, adder, and register are configured to receive a series of digital values in response to the series of input bits, shift each digital value in the series of digital values based on a bit location of an input bit within the series of input bits, and add results of the shift operations to generate an output indicating values stored in the selected memory cells.
In one example, a circuit comprises an input transistor comprising a first terminal, a second terminal coupled to ground, and a gate; a capacitor comprising a first terminal and a second terminal; an output transistor comprising a first terminal providing an output current, a second terminal coupled to ground, and a gate; a first switch; and a second switch; wherein in a first mode, the first switch is closed and couples an input current to the first terminal of the input transistor and the gate of the input transistor and the second switch is closed and couples the first terminal of the input transistor to the first terminal of the capacitor and the gate of the output transistor, and in a second mode, the first switch is open and the second switch is open and the capacitor discharges into the gate of the output transistor.
In one example, a system comprises a neural network array of non-volatile memory cells arranged in rows and columns; and a logical cell comprising a first plurality of non-volatile memory cells in a first row of the array and a second plurality of non-volatile memory cells in a second row adjacent to the first row; wherein the first plurality of non-volatile memory cells and the second plurality of non-volatile memory cells are configured as one or more coarse cells and one or more fine cells.
G11C 11/56 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using storage elements with more than two stable states represented by steps, e.g. of voltage, current, phase, frequency
G11C 16/04 - Erasable programmable read-only memories electrically programmable using variable threshold transistors, e.g. FAMOS
G11C 16/14 - Circuits for erasing electrically, e.g. erase voltage switching circuits
G11C 16/34 - Determination of programming status, e.g. threshold voltage, overprogramming or underprogramming, retention
73.
DETERMINATION OF A BIAS VOLTAGE TO APPLY TO ONE OR MORE MEMORY CELLS IN A NEURAL NETWORK
A first example comprises programming a memory cell to store a value; applying a series of currents of increasing size to a bit line of the memory cell; and measuring a voltage of a control gate terminal of the memory cell to determine a bias. A second example comprises programming a memory cell to store a value; applying a predetermined current to a bit line of the memory cell; and measuring a voltage of a control gate terminal of the memory cell to determine a bias.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
G11C 16/04 - Erasable programmable read-only memories electrically programmable using variable threshold transistors, e.g. FAMOS
In one example, a method comprises programming a memory cell capable of storing any of N values with 1 of the N values; applying a series of currents of increasing size to a bit line of the memory cell; comparing a voltage of the bit line to a reference voltage to generate a comparison output; and when the comparison output changes value, measuring a voltage of a control gate terminal of the memory cell and storing the voltage in a bias lookup table.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
G11C 16/04 - Erasable programmable read-only memories electrically programmable using variable threshold transistors, e.g. FAMOS
In one example, a method comprises performing a first programming process on a selected non-volatile memory cell, the first programming process comprising a plurality of program-verify cycles, wherein a programming voltage duration of increasing period is applied to one of a floating gate, a control gate terminal, an erase gate terminal, and a source line terminal of the selected non-volatile memory cell in each program-verify cycle after the first program-verify cycle.
G11C 16/34 - Determination of programming status, e.g. threshold voltage, overprogramming or underprogramming, retention
G11C 16/26 - Sensing or reading circuitsData output circuits
G11C 11/56 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using storage elements with more than two stable states represented by steps, e.g. of voltage, current, phase, frequency
G11C 16/14 - Circuits for erasing electrically, e.g. erase voltage switching circuits
G06N 3/044 - Recurrent networks, e.g. Hopfield networks
A method for screening memory cells includes erasing the memory cells, weakly programming the memory cells to a modified erased state, performing a first read operation on the memory cells after the erasing and the weakly programming, screening any of the memory cells that exhibit a read current during the first read operation below a margin read current threshold M1, baking the memory cells after the first read operation, performing a second read operation on the memory cells after the baking, and screening any of the memory cells that exhibit a read current during the second read operation below the margin read current threshold M1.
A method for screening memory cells includes erasing the memory cells, weakly programming the memory cells to a modified erased state, performing a first read operation on the memory cells after the erasing and the weakly programming, screening any of the memory cells that exhibit a read current during the first read operation below a margin read current threshold M1, baking the memory cells after the first read operation, performing a second read operation on the memory cells after the baking, and screening any of the memory cells that exhibit a read current during the second read operation below the margin read current threshold M1.
Numerous examples are disclosed of an artificial neural network that comprises vector-by-matrix multiplication arrays utilizing analog inputs. In one example, a system comprises a vector by matrix multiplication array comprising a plurality of non-volatile memory cells arranged in rows and columns, a capacitor comprising a first terminal and a second terminal, the second terminal coupled to a common potential, a row decoder to enable an application of an input signal to the first terminal of the capacitor in response to an address, and a buffer coupled to the first terminal of the capacitor, the buffer to generate an output voltage for a respective row of the vector by matrix multiplication array.
Numerous examples are disclosed of an artificial neural network that comprises vector-by-matrix multiplication arrays utilizing analog inputs. In one example, a system comprises a vector by matrix multiplication array comprising a plurality of non-volatile memory cells arranged in rows and columns, a capacitor comprising a first terminal and a second terminal, the second terminal coupled to a common potential, a row decoder to enable an application of an input signal to the first terminal of the capacitor in response to an address, and a buffer coupled to the first terminal of the capacitor, the buffer to generate an output voltage for a respective row of the vector by matrix multiplication array.
G06N 3/04 - Architecture, e.g. interconnection topology
G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
80.
VECTOR-BY-MATRIX-MULTIPLICATION ARRAY UTILIZING ANALOG OUTPUTS
Numerous examples are disclosed of an artificial neural network that comprises vector-by-matrix multiplication arrays utilizing analog outputs. In one example, a system comprises a vector by matrix multiplication array comprising a plurality of non-volatile memory cells arranged in rows and columns; and an output circuit to receive a respective neuron current from respective columns of the vector by matrix multiplication array and to generate a respective output voltage, the output circuit comprising a neuron scalar to generate a scaled current from the received respective neuron current, and a current-to-voltage converter to convert the scaled current into the respective output voltage.
Numerous examples are disclosed of an artificial neural network comprising a three-dimensional integrated circuit. In one embodiment, a three-dimensional integrated circuit for use in an artificial neural network comprises a first die comprising a first vector by matrix multiplication array and a first input multiplexor, the first die located on a first vertical layer; a second die comprising an input circuit, the second die located on a second vertical layer different than the first vertical layer; and one or more vertical interfaces coupling the first die and the second die; wherein during a read operation, the input circuit provides an input signal to the first input multiplexor over at least one of the one or more vertical interfaces, the first input multiplexor applies the input signal to one or more rows in the first vector by matrix multiplication array, and the first vector by matrix multiplication array generates an output.
Numerous examples are disclosed of an artificial neural network comprising a plurality of reference arrays used for configuration of a vector-by-matrix multiplication array. In one example, a system comprises a vector-by-matrix multiplication array in an artificial neural network; and a plurality of reference arrays characterized by different I-V curves, wherein one or more of the plurality of reference arrays are used to generate input voltage the vector-by-matrix multiplication array during operation.
Numerous examples are disclosed of an artificial neural network comprising a plurality of reference arrays used for configuration of a vector-by-matrix multiplication array. In one example, a system comprises a vector-by-matrix multiplication array in an artificial neural network; and a plurality of reference arrays characterized by different I-V curves, wherein one or more of the plurality of reference arrays are used to generate input voltage the vector-by-matrix multiplication array during operation.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G11C 7/14 - Dummy cell managementSense reference voltage generators
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
G11C 17/10 - Read-only memories programmable only onceSemi-permanent stores, e.g. manually-replaceable information cards using semiconductor devices, e.g. bipolar elements in which contents are determined during manufacturing by a predetermined arrangement of coupling elements, e.g. mask-programmable ROM
G11C 16/04 - Erasable programmable read-only memories electrically programmable using variable threshold transistors, e.g. FAMOS
G11C 29/02 - Detection or location of defective auxiliary circuits, e.g. defective refresh counters
G11C 29/24 - Accessing extra cells, e.g. dummy cells or redundant cells
Numerous examples are disclosed of an artificial neural network comprising a three-dimensional integrated circuit. In one embodiment, a three-dimensional integrated circuit for use in an artificial neural network comprises a first die comprising a first vector by matrix multiplication array and a first input multiplexor, the first die located on a first vertical layer; a second die comprising an input circuit, the second die located on a second vertical layer different than the first vertical layer; and one or more vertical interfaces coupling the first die and the second die; wherein during a read operation, the input circuit provides an input signal to the first input multiplexor over at least one of the one or more vertical interfaces, the first input multiplexor applies the input signal to one or more rows in the first vector by matrix multiplication array, and the first vector by matrix multiplication array generates an output.
Numerous examples are disclosed of an artificial neural network that comprises vector-by-matrix multiplication arrays utilizing analog outputs. In one example, a system comprises a vector by matrix multiplication array comprising a plurality of non-volatile memory cells arranged in rows and columns; and an output circuit to receive a respective neuron current from respective columns of the vector by matrix multiplication array and to generate a respective output voltage, the output circuit comprising a neuron scalar to generate a scaled current from the received respective neuron current, and a current-to-voltage converter to convert the scaled current into the respective output voltage.
G06N 3/04 - Architecture, e.g. interconnection topology
G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
G11C 7/10 - Input/output [I/O] data interface arrangements, e.g. I/O data control circuits, I/O data buffers
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
86.
CALIBRATION OF ELECTRICAL PARAMETERS IN A DEEP LEARNING ARTIFICIAL NEURAL NETWORK
Numerous examples are disclosed for performing calibration of various electrical parameters in a deep learning artificial neural network. In one example, a method comprises adjusting a bias voltage applied to one or more non-volatile memory cells in an artificial neural network, performing a performance target check on the one or more non-volatile memory cells in the artificial neural network, and repeating the adjusting and performing until the performance target check indicates an electrical parameter is within a predetermined range.
A method includes recessing an upper surface of a substrate in first and second areas relative to a third area, forming a first conductive layer in the first area, forming a second conductive layer in the three areas, selectively removing the first and second conductive layers in the first area, while maintaining the second conductive layer in the second and third areas, leaving pairs of stack structures in the first area respectively having a control gate of the second conductive layer and a floating gate of the first conductive layer, forming a third conductive layer in the three areas, recessing the upper surface of the third conductive layer below tops of the stack structures and removing the third conductive layer from the second and third areas, removing the second conductive layer from the second and third areas, and forming blocks of metal material in the second and third areas.
A method of forming a device on a silicon substrate having first, second and third areas includes recessing an upper substrate surface in the first and third areas, forming an upwardly extending silicon fin in the second area, forming first source, drain and channel regions in the first area, forming second source, drain and channel regions in the fin, forming third source, drain and channel regions in the third area, forming a floating gate over a first portion of the first channel region using a first polysilicon deposition, forming an erase gate over the first source region and a device gate over the third channel region using a second polysilicon deposition, and forming a word line gate over a second portion of the first channel region, a control gate over the floating gate, and a logic gate over the second channel region using a metal deposition.
H01L 29/08 - Semiconductor bodies characterised by the shapes, relative sizes, or dispositions of the semiconductor regions with semiconductor regions connected to an electrode carrying current to be rectified, amplified, or switched and such electrode being part of a semiconductor device which comprises three or more electrodes
H01L 29/788 - Field-effect transistors with field effect produced by an insulated gate with floating gate
H01L 29/423 - Electrodes characterised by their shape, relative sizes or dispositions not carrying the current to be rectified, amplified or switched
H01L 29/78 - Field-effect transistors with field effect produced by an insulated gate
H01L 27/11556 - Electrically programmable read-only memories; Multistep manufacturing processes therefor with floating gate characterised by three-dimensional arrangements, e.g. with cells on different height levels with source and drain on different levels, e.g. with sloping channels the channels comprising vertical portions, e.g. U-shaped channels
89.
METHOD OF FORMING A DEVICE WITH PLANAR SPLIT GATE NON-VOLATILE MEMORY CELLS, PLANAR HV DEVICES, AND FINFET LOGIC DEVICES ON A SUBSTRATE
A method of forming a device on a silicon substrate having first, second and third areas includes recessing an upper substrate surface in the first and third areas, forming an upwardly extending silicon fin in the second area, forming first source, drain and channel regions in the first area, forming second source, drain and channel regions in the fin, forming third source, drain and channel regions in the third area, forming a floating gate over a first portion of the first channel region using a first polysilicon deposition, forming an erase gate over the first source region and a device gate over the third channel region using a second polysilicon deposition, and forming a word line gate over a second portion of the first channel region, a control gate over the floating gate, and a logic gate over the second channel region using a metal deposition.
A method includes recessing an upper surface of a substrate in first and second areas relative to a third area, forming a first conductive layer in the first area, forming a second conductive layer in the three areas, selectively removing the first and second conductive layers in the first area, while maintaining the second conductive layer in the second and third areas, leaving pairs of stack structures in the first area respectively having a control gate of the second conductive layer and a floating gate of the first conductive layer, forming a third conductive layer in the three areas, recessing the upper surface of the third conductive layer below tops of the stack structures and removing the third conductive layer from the second and third areas, removing the second conductive layer from the second and third areas, and forming blocks of metal material in the second and third areas.
In one example, a method comprises determining a program resolution current value; and setting levels for a programming operation of a plurality of non-volatile memory cells in a neural network array such that a delta current between levels of each pair of adjacent cells in the plurality is a multiple of the program resolution current value.
G11C 11/56 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using storage elements with more than two stable states represented by steps, e.g. of voltage, current, phase, frequency
G11C 16/04 - Erasable programmable read-only memories electrically programmable using variable threshold transistors, e.g. FAMOS
G11C 16/14 - Circuits for erasing electrically, e.g. erase voltage switching circuits
G11C 16/34 - Determination of programming status, e.g. threshold voltage, overprogramming or underprogramming, retention
A memory device includes a non-volatile memory cells, source regions and drain regions arranged in rows and columns. Respective ones of the columns of drain regions include first drain regions and second drain regions that alternate with each other. Respective ones of first lines electrically connect together the source regions in one of the rows of the source regions and are electrically isolated from the source regions in other rows of the source regions. Respective ones of second lines electrically connect together the first drain regions of one of the columns of drain regions and are electrically isolated from the second drain regions of the one column of drain regions. Respective ones of third lines electrically connect together the second drain regions of one of the columns of drain regions and are electrically isolated from the first drain regions of the one column of drain regions.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G06F 3/06 - Digital input from, or digital output to, record carriers
G06N 3/04 - Architecture, e.g. interconnection topology
Numerous examples are disclosed for performing calibration of various electrical parameters in a deep learning artificial neural network. In one example, a method comprises adjusting a bias voltage applied to one or more non-volatile memory cells in an artificial neural network, performing a performance target check on the one or more non-volatile memory cells in the artificial neural network, and repeating the adjusting and performing until the performance target check indicates an electrical parameter is within a predetermined range.
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G11C 29/02 - Detection or location of defective auxiliary circuits, e.g. defective refresh counters
94.
METHOD OF FORMING A SEMICONDUCTOR DEVICE WITH MEMORY CELLS, HIGH VOLTAGE DEVICES AND LOGIC DEVICES ON A SUBSTRATE USING A DUMMY AREA
A method of forming a device on a semiconductor substrate having first, second, third and dummy areas, includes recessing the substrate upper surface in the first, second and dummy areas, forming a first conductive layer over the substrate, removing the first conductive layer from the third area and a second portion of the dummy area, forming a first insulation layer over the substrate, forming first trenches through the first insulation layer and into the substrate in the third area and the second portion of the dummy area, forming second trenches through the first insulation layer, the first conductive layer and into the substrate in the first and second areas and a first portion of the dummy area, and filling the first and second trenches with insulation material. Then, memory cells are formed in the first area, HV devices in the second area and logic devices in the third area.
A method of forming a device on a semiconductor substrate having first, second, third and dummy areas, includes recessing the substrate upper surface in the first, second and dummy areas, forming a first conductive layer over the substrate, removing the first conductive layer from the third area and a second portion of the dummy area, forming a first insulation layer over the substrate, forming first trenches through the first insulation layer and into the substrate in the third area and the second portion of the dummy area, forming second trenches through the first insulation layer, the first conductive layer and into the substrate in the first and second areas and a first portion of the dummy area, and filling the first and second trenches with insulation material. Then, memory cells are formed in the first area, HV devices in the second area and logic devices in the third area.
H10B 41/35 - Electrically erasable-and-programmable ROM [EEPROM] devices comprising floating gates characterised by the memory core region with a cell select transistor, e.g. NAND
H10B 41/41 - Electrically erasable-and-programmable ROM [EEPROM] devices comprising floating gates characterised by the peripheral circuit region of a memory region comprising a cell select transistor, e.g. NAND
H01L 29/423 - Electrodes characterised by their shape, relative sizes or dispositions not carrying the current to be rectified, amplified or switched
96.
CALIBRATION OF ELECTRICAL PARAMETERS IN A DEEP LEARNING ARTIFICIAL NEURAL NETWORK
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G06N 3/04 - Architecture, e.g. interconnection topology
97.
Method Of Scanning An Image Using Non-volatile Memory Array Neural Network Classifier
A method of scanning N×N pixels using a vector-by-matrix multiplication array by (a) associating a filter of M×M pixels adjacent first vertical and horizontal edges, (b) providing values for the pixels associated with different respective rows of the filter to input lines of different respective N input line groups, (c) shifting the filter horizontally by X pixels, (d) providing values for the pixels associated with different respective rows of the horizontally shifted filter to input lines, of different respective N input line groups, which are shifted by X input lines, (e) repeating steps (c) and (d) until a second vertical edge is reached, (f) shifting the filter horizontally to be adjacent the first vertical edge, and shifting the filter vertically by X pixels, (g) repeating steps (b) through (e) for the vertically shifted filter, and (h) repeating steps (f) and (g) until a second horizontal edge is reached.
G06N 3/04 - Architecture, e.g. interconnection topology
G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G11C 16/34 - Determination of programming status, e.g. threshold voltage, overprogramming or underprogramming, retention
Numerous examples are disclosed for performing calibration of various electrical parameters in a deep learning artificial neural network. In one example, a system comprises a digital-to-analog converter for receiving an input of k bits and generating a first analog output, a mapping scalar for converting the first analog output into a second analog output, and an analog-to-digital converter for generating an output of n bits from the second analog output, where n is a different value than k.
Numerous examples are described for providing an artificial neural network system comprising an analog array and a digital array. In certain examples, an analog array and a digital array are coupled to shared bit lines. In other examples, an analog array and a digital array are coupled to separate bit lines.
G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
G06N 3/04 - Architecture, e.g. interconnection topology
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
G11C 11/56 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using storage elements with more than two stable states represented by steps, e.g. of voltage, current, phase, frequency
G06F 3/06 - Digital input from, or digital output to, record carriers
100.
ARTIFICIAL NEURAL NETWORK COMPRISING AN ANALOG ARRAY AND A DIGITAL ARRAY
Numerous examples are described for providing an artificial neural network system comprising an analog array and a digital array. In certain examples, an analog array and a digital array are coupled to shared bit lines. In other examples, an analog array and a digital array are coupled to separate bit lines.
G06N 3/04 - Architecture, e.g. interconnection topology
G11C 11/54 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using elements simulating biological cells, e.g. neuron
