Silicon Storage Technology, Inc.

Other

Thuan Vu

Hieu Van Tran

Anh Ly

Stanley Hong

Hung Quoc Nguyen

Stephen Trinh

Nhan Do

Vipin Tiwari

Sang Thanh Nguyen

Loc Bao Hoang

Hien Pham

Steven Lemke

Kha Nguyen

Han Tran

George Spector

Vishal Sarin

Nasrin Jaffari

Steve Choi

Sheng-Hsiung Hsueh

Wingfu AAron Lau

Xian Liu

Mark Reiten

Nghia Le

Nha Nguyen

Jack Frayer

Alexander Kotov

Eugene Jinglun Tam

Tho Ngoc Dang

Michael James Heinz

Jung Hee Yun

Michael K Doan

Feng Zhou

Hoa Vu

Duc Nguyen

Viet Tan Nguyen

Toan Le

Yuri Tkachev

Henry A Om'Mani

Hung O Nguyen

Fan Luo

Louisa Schneider

Susmita Karmakar

Ca Stanley Hong

Stephen T Trinh

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.

Adaptive bias decoder for non-volatile memory system

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.

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.

Current-to-voltage converter comprising common mode circuit

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.

Output block for array of non-volatile memory cells

Numerous embodiments are disclosed for splitting an array of non-volatile memory cells in an analog neural memory in a deep learning artificial neural network into multiple parts. Each part of the array interacts with certain circuitry dedicated to that part and with other circuitry that is shared with one or more other parts of the array.

Split array architecture for analog neural memory in a deep learning artificial neural network

Configurable input blocks and output blocks and physical layouts are disclosed for analog neural memory systems that utilize non-volatile memory cells. An input block can be configured to support different numbers of arrays arranged in a horizontal direction, and an output block can be configured to support different numbers of arrays arranged in a vertical direction. Adjustable components are disclosed for use in the configurable input blocks and output blocks. Systems and methods are utilized for compensating for leakage and offset in the input blocks and output blocks the in analog neural memory systems.

Output array neuron conversion and calibration for analog neural memory in deep learning artificial neural network

Numerous embodiments of analog neural memory arrays are disclosed. Certain embodiments contain improved mechanisms for pulling source lines down to ground expeditiously. This is useful, for example, to minimize the voltage drop for a read, program, or erase operation.

Analog neural memory array in artificial neural network with source line pulldown mechanism

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.

Input and output blocks for an array of memory cells

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.

Neural network device

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.

Multiple row programming operation in artificial neural network array

Various examples of decoders and physical layout designs for non-volatile flash memory arrays in an analog neural system are disclosed. In one example, a system comprises a plurality of vector-by-matrix multiplication arrays in an analog neural memory system, each vector-by-matrix multiplication array comprising an array of non-volatile memory cells organized into rows and columns, wherein each memory cell comprises a word line terminal; a plurality of read row decoders, each read row decoder coupled to one of the plurality of vector-by-matrix multiplication arrays for applying a voltage to one or more selected rows during a read operation; and a shared program row decoder coupled to all of the plurality of vector-by-matrix multiplication arrays for applying a voltage to one or more selected rows in one or more of the vector-by-matrix multiplication arrays during a program operation.

Read and programming decoding system for analog neural memory

In one example, a method of testing a plurality of non-volatile memory cells in an array of non-volatile memory cells, wherein the array is arranged in rows and columns, wherein each row is coupled to a word line and each column is coupled to a bit line, and wherein each word line is selectively coupled to a row decoder and each bit line is selectively coupled to a column decoder, comprises asserting, by the row decoder, all word lines in the array; asserting, by the column decoder, all bit lines in the array; performing a deep programming operation on the array of non-volatile memory cells; and measuring a total current received from the bit lines.

Testing of analog neural memory cells in an artificial neural network

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.

Setting levels for a programming operation in a neural network array

Numerous embodiments of decoders for use with a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. The decoders include bit line decoders, word line decoders, control gate decoders, source line decoders, and erase gate decoders. In certain embodiments, a high voltage version and a low voltage version of a decoder is used.

Decoders for analog neural memory in deep learning artificial neural network

Numerous embodiments for reading or verifying a value stored in a selected memory cell in a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. In one embodiment, an input comprises a set of input bits that result in a series of input signals applied to a terminal of the selected memory cell, further resulting in a series of output signals that are digitized, shifted based on the bit location of the corresponding input bit in the set of input bits, and added to yield an output indicating a value stored in the selected memory cell.

Input and digital output mechanisms for analog neural memory in a deep learning artificial neural network

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San Jose, CA, United States of America

Thuan Vu