Abstract: 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, and second and third gates over the floating gate and over 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 third gates in one of the memory cell rows, fourth lines each electrically connect the source regions in one of the memory cell rows, and fifth 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, second or third lines, and provide a first plurality of outputs as electrical currents on the fifth lines.

Abstract: Numerous embodiments are disclosed for providing temperature compensation and leakage compensation for an analog neuromorphic memory system used in a deep learning neural network. The embodiments for providing temperature compensation implement discreet or continuous adaptive slope compensation and renormalization for devices, reference memory cells, or selected memory cells in the memory system. The embodiments for providing leakage compensation within a memory cell in the memory system implement adaptive erase gate coupling or the application of a negative bias on a control gate terminal, a negative bias on a word line terminal, or a bias on a source line terminal.

Abstract: Numerous embodiments of a precision tuning algorithm and apparatus are disclosed for precisely and quickly depositing the correct amount of charge on the floating gate of a non-volatile memory cell within a vector-by-matrix multiplication (VMM) array in an artificial neural network. Selected cells thereby can be programmed with extreme precision to hold one of N different values.

Abstract: A memory device includes a plurality of memory cells and a controller. The controller is configured to program each of the memory cells to one of a plurality of program states, and to read the memory cells using a read operation of applied voltages to the memory cells. During the read operation, separations between adjacent ones of the program states vary based on frequencies of use of the program states in the plurality of memory cells.

Abstract: A neural network device with synapses having memory cells each having source and drain regions in a semiconductor substrate with a channel region extending there between, a floating gate over an entirety of the channel region, and a first gate over the floating gate. First lines each electrically connect together the first gates in one of the memory cell rows, second lines each electrically connect together the source regions in one of the memory cell rows, and third lines each electrically connect together the drain regions in one of the memory cell columns. The synapses are configured to receive a first plurality of inputs as electrical voltages on the first lines or on the second lines, and to provide a first plurality of outputs as electrical currents on the third lines.

Abstract: Numerous embodiments of power management techniques are disclosed for various operations involving one or more vector-by-matrix multiplication (VMM) arrays within an artificial neural network.

Abstract: Numerous embodiments are disclosed for converting neuron current output by a vector-by-matrix multiplication (VMM) array into neuron current-based time pulses and providing such pulses as an input to another VMM array within an artificial neural network. Numerous embodiments are disclosed for converting the neuron current-based time pulses into analog current or voltage values if an analog input is needed for the VMM array.

Abstract: 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 columns, 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 second lines, and provide a first plurality of outputs as electrical currents on the third or fourth lines.

Abstract: A memory device includes a plurality of memory cells and a controller. The controller is configured to program each of the memory cells to one of a plurality of program states, and to read the memory cells using a read operation of applied voltages to the memory cells. During the read operation, separations between adjacent ones of the program states vary based on frequencies of use of the program states in the plurality of memory cells.

Abstract: A neural network device having a first plurality of synapses that includes a plurality of memory cells. Each memory cell includes a floating gate over a first portion of a channel region and a first gate over a second portion of the channel region. The memory cells are arranged in rows and columns. A plurality of first lines each electrically connect together the first gates in one of the memory cell rows, a plurality of second lines each electrically connect together the source regions in one of the memory cell rows, and a plurality of third lines each electrically connect together the drain regions in one of the memory cell columns. The first plurality of synapses receives a first plurality of inputs as electrical voltages on the plurality of third lines, and provides a first plurality of outputs as electrical currents on the plurality of second lines.

Abstract: An artificial neural network device that utilizes one or more non-volatile memory arrays as the synapses. The synapses are configured to receive inputs and to generate therefrom outputs. Neurons are configured to receive the outputs. The synapses include a plurality of memory cells, wherein each of the memory cells includes spaced apart source and drain regions formed in a semiconductor substrate with a channel region extending there between, a floating gate disposed over and insulated from a first portion of the channel region and a non-floating gate disposed over and insulated from a second portion of the channel region. Each of the plurality of memory cells is configured to store a weight value corresponding to a number of electrons on the floating gate. The plurality of memory cells are configured to multiply the inputs by the stored weight values to generate the outputs. Various algorithms for tuning the memory cells to contain the correct weight values are disclosed.

Abstract: Numerous embodiments of a precision tuning algorithm and apparatus are disclosed for precisely and quickly depositing the correct amount of charge on the floating gate of a non-volatile memory cell within a vector-by-matrix multiplication (VMM) array in an artificial neural network. Selected cells thereby can be programmed with extreme precision to hold one of N different values.

Abstract: A memory device includes rows and columns of memory cells, word lines each connected to a memory cell row, bit lines each connected to a memory cell column, a word line driver connected to the word lines, a bit line driver connected to the bit lines, word line switches each disposed on one of the word lines for selectively connecting one memory cell row to the word line driver, and bit line switches each disposed on one of the bit lines for selectively connecting one memory cell column to the bit line driver. A controller controls the word line switches to connect only some of the rows of memory cells to the word line driver at a first point in time, and controls the bit line switches to connect only some of the columns of memory cells to the bit line driver at a second point in time.

Abstract: Numerous embodiments are disclosed for a configurable hardware system for use in an analog neural memory system for a deep learning neural network. The components within the configurable hardware system that are configurable can include vector-by-matrix multiplication arrays, summer circuits, activation circuits, inputs, reference devices, neurons, and testing circuits. These devices can be configured to provide various layers or vector-by-matrix multiplication arrays of various sizes, such that the same hardware can be used in analog neural memory systems with different requirements.

Abstract: Numerous embodiments are disclosed for providing temperature compensation and leakage compensation for an analog neuromorphic memory system used in a deep learning neural network. The embodiments for providing temperature compensation implement discreet or continuous adaptive slope compensation and renormalization for devices, reference memory cells, or selected memory cells in the memory system. The embodiments for providing leakage compensation within a memory cell in the memory system implement adaptive erase gate coupling or the application of a negative bias on a control gate terminal, a negative bias on a word line terminal, or a bias on a source line terminal.

Abstract: A memory device includes rows and columns of memory cells, word lines each connected to a memory cell row, bit lines each connected to a memory cell column, a word line driver connected to the word lines, a bit line driver connected to the bit lines, word line switches each disposed on one of the word lines for selectively connecting one memory cell row to the word line driver, and bit line switches each disposed on one of the bit lines for selectively connecting one memory cell column to the bit line driver. A controller controls the word line switches to connect only some of the rows of memory cells to the word line driver at a first point in time, and controls the bit line switches to connect only some of the columns of memory cells to the bit line driver at a second point in time.

Abstract: Numerous embodiments of programming systems and methods for use with a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. Selected cells thereby can be programmed with extreme precision to hold one of N different values.

Abstract: A memory device that generates a unique identifying number, and includes a plurality of memory cells and a controller. Each of the memory cells includes first and second regions formed in a semiconductor substrate, wherein a channel region of the substrate extends between the first and second regions, a floating gate disposed over and insulated from a first portion of the channel region, and a select gate disposed over and insulated from a second portion of the channel region. The controller is configured to apply one or more positive voltages to the first regions of the memory cells while the memory cells are in a subthreshold state for generating leakage current through each of the channel regions, measure the leakage currents, and generate a number based on the measured leakage currents.

Abstract: A three-dimensional flash memory system is disclosed. The system comprises a memory array comprising a plurality of stacked dies, where each die comprises memory cells. The system further comprises a plurality of pins, where the function of at least some of the pins can be configured using a mechanism that selects a function for those pins from a plurality of possible functions.

Abstract: A neural network device having a first plurality of synapses that includes a plurality of memory cells. Each memory cell includes a floating gate over a first portion of a channel region and a first gate over a second portion of the channel region. The memory cells are arranged in rows and columns. A plurality of first lines each electrically connect together the first gates in one of the memory cell rows, a plurality of second lines each electrically connect together the source regions in one of the memory cell rows, and a plurality of third lines each electrically connect together the drain regions in one of the memory cell columns. The first plurality of synapses receives a first plurality of inputs as electrical voltages on the plurality of third lines, and provides a first plurality of outputs as electrical currents on the plurality of second lines.