Silicon Storage Technology, Inc.

University of California

Agency for Science, Technology and Research

Nhan Do

Hieu Van Tran

Vipin Tiwari

Xian Liu

Mark Reiten

Steven Lemke

Thuan Vu

Feng Zhou

Stanley Hong

Chunming Wang

Chien-Sheng Su

Anh Ly

Jinho Kim

Jeng-Wei Yang

Leo Xing

Stephen Trinh

Man-Tang Wu

Catherine Decobert

Serguei Jourba

Farnood Merrikh Bayat

Chun-Ming Chen

Dmitri Strukov

Xinjie Guo

Mandana Tadayoni

Yuri Tkachev

Melvin Diao

Parviz Ghazavi

Guo Xiang Song

Hung Quoc Nguyen

Andy Qiang Liu

Henry A Om'Mani

Nha Nguyen

Elizabeth Cuevas

Amitay Levi

Bruno Villard

Gilles Festes

Jack Sun

Bernard Bertello

Alexander Kotov

Xiaozhou Qian

Prateep Tuntasood

Gerhard Metzger-Brueckl

Kai Man Yue

Zhixian Chen

Ning Bai

Andy Yang

Yueh-Hsin Chen

Stephan Wege

Cecile Bernardi

Liz Cuevas

Hsuan Liang

Florence Guyot

Zhuoqiang Jia

Henry Om'mani

Guo Yong Liu

Santosh Hariharan

Louisa Schneider

Jean Francois Thiery

Willem-Jan Toren

Nadia Miridi

Jong-Won Yoo

Xinpeng Wang

Guangming Lin

Yaohua Zhu

Shiuh-Luen Wang

Fan Luo

Chien Sheng Su

Chieng-Sheng Su

Man Tang Wu

Boolean Fan

Lihsin Chang

Cynthia Fung

Latt Tee

Chen-Chih Fan

Wang Xinpeng

Guo-Qiang Lo

Melvin Dao

Vlpin Tiwari

Zhou Feng

Vipln Tiwari

Chung-Ming Chen

Steven Malcolm Lemke

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.

Adaptive bias decoder for non-volatile memory system

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.

Method of forming a device with planar split gate non-volatile memory cells, planar HV devices, and FinFET logic devices on a substrate

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.

Neural network classifier using array of three-gate non-volatile memory cells

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

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.

Memory device of non-volatile memory cells

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.

Deep learning neural network classifier using non-volatile memory array

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.

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.

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

Numerous examples of an input function circuit block and an output neuron circuit block coupled to a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. In one example, an artificial neural network comprises a vector-by-matrix multiplication array comprising a plurality of non-volatile memory cells organized into rows and columns; an input function circuit block to receive digital input signals, convert the digital input signals into analog signals, and apply the analog signals to control gate terminals of non-volatile memory cells in one or more rows of the array during a programming operation; and an output neuron circuit block to receive analog currents from the columns of the array during a read operation and generate an output signal.

Input function circuit block and output neuron circuit block coupled to a vector-by-matrix multiplication array in an artificial neural network

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.

Output circuit

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

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.

Method of forming a semiconductor device with memory cells, high voltage devices and logic devices on a substrate using a dummy area

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.

Memory cell array with row direction gap between erase gate lines and dummy floating gates

Numerous embodiments of a precision programming 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.

Precise data tuning method and apparatus for analog neural memory in an artificial neural network

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Saratoga, CA, United States of America

Nhan Do