IP Library Granted Patent US 12,300,313
Granted Patent B2
US 12,300,313 · App. 17/580,862 · Granted May 13, 2025

Deep learning neural network classifier using non-volatile memory array

Inventors: Farnood Merrikh Bayat (Goleta, CA); Xinjie Guo (Goleta, CA); Dmitri Strukov (Goleta, CA); Nhan Do (Saratoga, CA); Hieu Van Tran (San Jose, CA); Vipin Tiwari (Dublin, CA); Mark Reiten (Alamo, CA)
Assignees: Silicon Storage Technology, Inc.; The Regents of the University of California
G11C11/54G06F3/061G06F3/0655G06F3/0688G06N3/04G06N3/045G06N3/063G11C16/08G11C16/12G11C16/16G11C16/3436G11C29/38
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,300,313
App. No.
17/580,862
Granted
May 13, 2025
Kind
B2
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.

Claims (56)

1. A neural network device, comprising:

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, 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, a first non-floating gate disposed over and insulated from a second portion of the channel region and configured to receive the first plurality of inputs, a second non-floating gate disposed over and insulated from the source region, and a third non-floating gate disposed over and insulated from the floating gate;

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 first plurality of inputs by the stored weight values to generate the first plurality of outputs;

a first plurality of neurons configured to receive the first plurality of outputs;

wherein the memory cells of the first plurality of synapses are arranged in rows and columns, and wherein the first plurality of synapses comprises:

a plurality of first lines each electrically connecting together the first non-floating gates in one of the rows of the memory cells and physically isolated from the first non-floating gates in other rows of the memory cells;

a plurality of second lines each electrically connecting together the second non-floating gates in one of the columns of the memory cells and physically isolated from the second non-floating gates in other columns of the memory cells;

a plurality of third lines each electrically connecting together the third non-floating gates in one of the rows of the memory cells and physically isolated from the third non-floating gates in other rows of the memory cells;

a plurality of fourth lines each electrically connecting together the source regions in one of the rows of the memory cells and physically isolated from the source regions in other rows of the memory cells;

a plurality of fifth lines each electrically connecting together the drain regions in one of the columns of the memory cells and physically isolated from the drain regions in other columns of the memory cells;

wherein the first plurality of synapses is configured to receive the first plurality of inputs on the plurality of first lines, and to provide the first plurality of outputs on the plurality of fifth lines.

2. The neural network device of claim 1 , wherein for each of the plurality of fifth lines, one of the first plurality of outputs is provided thereon which is a sum of currents through the memory cells multiplied by respective weight values stored in the memory cells, for all of the memory cells in the one column of the memory cells.

3. The neural network device of claim 1 , further comprising:

circuitry for logarithmically converting current of the first plurality of inputs to voltage before the receiving of the first plurality of inputs on the plurality of first lines.

4. A neural network device, comprising:

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, 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, a first non-floating gate disposed over and insulated from a second portion of the channel region and configured to receive the first plurality of inputs, a second non-floating gate disposed over and insulated from the source region, and a third non-floating gate disposed over and insulated from the floating gate;

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 first plurality of inputs by the stored weight values to generate the first plurality of outputs;

a first plurality of neurons configured to receive the first plurality of outputs;

wherein the memory cells of the first plurality of synapses are arranged in rows and columns, and wherein the first plurality of synapses comprises:

a plurality of first lines each electrically connecting together the first non-floating gates in one of the rows of the memory cells and physically isolated from the first non-floating gates in other rows of the memory cells;

a plurality of second lines each electrically connecting together the second non-floating gates in one of the rows of the memory cells and physically isolated from the second non-floating gates in other rows of the memory cells;

a plurality of third lines each electrically connecting together the third non-floating gates in one of the rows of the memory cells and physically isolated from the third non-floating gates in other rows of the memory cells;

a plurality of fourth lines each electrically connecting together the source regions in one of the columns of the memory cells and physically isolated from the source regions in other columns of the memory cells;

a plurality of fifth lines each electrically connecting together the drain regions in one of the columns of the memory cells and physically isolated from the drain regions in other columns of the memory cells;

wherein the first plurality of synapses is configured to receive the first plurality of inputs on the plurality of first lines, and to provide the first plurality of outputs on the plurality of fifth lines.

5. The neural network device of claim 4 , wherein for each of the plurality of fifth lines, one of the first plurality of outputs is provided thereon which is a sum of currents through the memory cells multiplied by respective weight values stored in the memory cells, for all of the memory cells in the one column of the memory cells.

6. A neural network device, comprising:

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, 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, a first non-floating gate disposed over and insulated from a second portion of the channel region and configured to receive the first plurality of inputs, a second non-floating gate disposed over and insulated from the source region, and a third non-floating gate disposed over and insulated from the floating gate;

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 first plurality of inputs by the stored weight values to generate the first plurality of outputs;

a first plurality of neurons configured to receive the first plurality of outputs;

wherein the memory cells of the first plurality of synapses are arranged in rows and columns, and wherein the first plurality of synapses comprises:

a plurality of first lines each electrically connecting together the first non-floating gates in one of the rows of the memory cells and physically isolated from the first non-floating gates in other rows of the memory cells;

a plurality of second lines each electrically connecting together the second non-floating gates in one of the rows of the memory cells and physically isolated from the second non-floating gates in other rows of the memory cells;

a plurality of third lines each electrically connecting together the third non-floating gates in one of the rows of the memory cells and physically isolated from the third non-floating gates in other rows of the memory cells;

a plurality of fourth lines each electrically connecting together the source regions in one of the columns of the memory cells and physically isolated from the source regions in other columns of the memory cells;

a plurality of fifth lines each electrically connecting together the drain regions in one of the columns of the memory cells and physically isolated from the drain regions in other columns of the memory cells;

wherein the first plurality of synapses is configured to receive the first plurality of inputs on the plurality of first lines, and to provide the first plurality of outputs on the plurality of fourth lines.

7. The neural network device of claim 6 , wherein for each of the plurality of fourth lines, one of the first plurality of outputs is provided thereon which is a sum of currents through the memory cells multiplied by respective weight values stored in the memory cells, for all of the memory cells in the one column of the memory cells.

8. A neural network device, comprising:

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, 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 first non-floating gate disposed over and insulated from a second portion of the channel region and configured to receive the first plurality of inputs;

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 first plurality of inputs by the stored weight values to generate the first plurality of outputs;

a first plurality of neurons configured to receive the first plurality of outputs;

wherein the memory cells of the first plurality of synapses are arranged in rows and columns, and wherein the first plurality of synapses comprises:

a plurality of first lines each electrically connecting together the first non-floating gates in one of the rows of the memory cells and physically isolated from the first non-floating gates in other rows of the memory cells;

a plurality of second lines each electrically connecting together the source regions in one of the columns of the memory cells and physically isolated from the source regions in other columns of the memory cells;

a plurality of third lines each electrically connecting together the drain regions in one of the columns of the memory cells and physically isolated from the drain regions in other columns of the memory cells;

wherein the first plurality of synapses is configured to receive the first plurality of inputs on the plurality of first lines, and to provide the first plurality of outputs on the plurality of third lines.

9. The neural network device of claim 8 , wherein for each of the plurality of third lines, one of the first plurality of outputs is provided thereon which is a sum of currents through the memory cells multiplied by respective weight values stored in the memory cells, for all of the memory cells in the one column of the memory cells.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 25, 2025
From: STRUKOV, DMITRI B.; GUO, XINJIE; BAYAT, FARNOOD MERRIKH; DO, NHAN; TRAN, HIEU VAN; TIWARI, VIPIN; REITEN, MARK
To: SILICON STORAGE TECHNOLOGY, INC.; THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
Reel/Frame 070945/0480 →
Continuity (3)
Continuation 15594439 · May 12, 2017
Provisional Application 62337760 · May 17, 2016
Related Publication 20220147794A1 · May 12, 2022
References Cited (153)
US 4810910A · Schoellikopf · 1989 [cited by applicant]
US 4904881A · Castro · 1990 [cited by applicant]
US 4950917A · Holler · 1990 [cited by applicant]
US 4961002A · Tam · 1990 [cited by applicant]
US 5028810A · Castro · 1991 [cited by applicant]
US 5029130A · Yeh · 1991 [cited by applicant]
US 5055897A · Canepa · 1991 [cited by applicant]
US 5097441A · Cho · 1992 [cited by applicant]
US 5138576A · Madurawe · 1992 [cited by applicant]
US 5146602A · Holler · 1992 [cited by applicant]
US 5150450A · Swenson · 1992 [cited by applicant]
US 5242848A · Yeh · 1993 [cited by applicant]
US 5256911A · Holler · 1993 [cited by applicant]
US 5264734A · Holler · 1993 [cited by applicant]
US 5298796A · Tawel · 1994 [cited by applicant]
US 5336936A · Allen · 1994 [cited by applicant]
US 5386132A · Wong · 1995 [cited by applicant]
US 5469397A · Hoshino · 1995 [cited by applicant]
US 5509106A · Pechanek · 1996 [cited by applicant]
US 5554874A · Doluca · 1996 [cited by applicant]
US 5621336A · Shibata · 1997 [cited by applicant]
US 5643814A · Chung · 1997 [cited by applicant]
US 5721702A · Briner · 1998 [cited by applicant]
US 5748534A · Dunlap · 1998 [cited by applicant]
US 5914894A · Diorio · 1999 [cited by applicant]
US 5966332A · Takano · 1999 [cited by applicant]
US 5990512A · Diorio · 1999 [cited by applicant]
US 6144584A · Kunori · 2000 [cited by applicant]
US 6222777B1 · Khieu · 2001 [cited by applicant]
US 6232180B1 · Chen · 2001 [cited by applicant]
US 6282119B1 · Tsen · 2001 [cited by applicant]
US 6389404B1 · Carson · 2002 [cited by applicant]
US 6683645B1 · Collins · 2004 [cited by applicant]
US 6747310B2 · Fan · 2004 [cited by examiner]
US 6829598B2 · Miley · 2004 [cited by applicant]
US 7315056B2 · Klinger · 2008 [cited by applicant]
US 7663916B2 · Chih · 2010 [cited by applicant]
US 7668013B2 · Chern · 2010 [cited by applicant]
US 7868375B2 · Liu et al. · 2011 [cited by applicant]
US 9430735B1 · Vali · 2016 [cited by examiner]
US 10217512B1 · Leobandung · 2019 [cited by applicant]
US 10269440B2 · Guo · 2019 [cited by applicant]
US 10474948B2 · Yakopcic · 2019 [cited by examiner]
US 10741611B1 · Leobandung · 2020 [cited by applicant]
US 20020089014A1 · Chern · 2002 [cited by applicant]
US 20030034510A1 · Liu · 2003 [cited by applicant]
US 20030052360A1 · Guterman · 2003 [cited by applicant]
US 20030183871A1 · Dugger et al. · 2003 [cited by applicant]
US 20040095809A1 · Sakamoto · 2004 [cited by applicant]
US 20040125655A1 · Tsai · 2004 [cited by applicant]
US 20040156241A1 · Tsai · 2004 [cited by applicant]
US 20040150032A1 · Wu · 2004 [cited by applicant]
US 20040155234A1 · Ishimaru · 2004 [cited by applicant]
US 20040251949A1 · Shi · 2004 [cited by applicant]
US 20050087892A1 · Hsu · 2005 [cited by applicant]
US 20050122238A1 · Nomura · 2005 [cited by applicant]
US 20060104120A1 · Hemink · 2006 [cited by applicant]
US 20060170038A1 · Wong · 2006 [cited by applicant]
US 20070171756A1 · Lambrache · 2007 [cited by applicant]
US 20080304345A1 · Kwean · 2008 [cited by applicant]
US 20090103361A1 · Wang · 2009 [cited by applicant]
US 20090109760A1 · Nazarian · 2009 [cited by applicant]
US 20090154246A1 · Liu · 2009 [cited by applicant]
US 20090192958A1 · Todorokihara · 2009 [cited by applicant]
US 20100046299A1 · Roohparvar · 2010 [cited by applicant]
US 20100290292A1 · Tanizaki · 2010 [cited by applicant]
US 20110161625A1 · Pechanek · 2011 [cited by applicant]
US 20110235419A1 · Ishimaru · 2011 [cited by applicant]
US 20120068872A1 · Baker · 2012 [cited by applicant]
US 20120087188A1 · Hsieh et al. · 2012 [cited by applicant]
US 20120136913A1 · Duong · 2012 [cited by applicant]
US 20130044544A1 · Shiino · 2013 [cited by applicant]
US 20130100756A1 · Liao et al. · 2013 [cited by applicant]
US 20140054667A1 · Tkachev · 2014 [cited by applicant]
US 20140140132A1 · Gu · 2014 [cited by applicant]
US 20140269062A1 · Do · 2014 [cited by applicant]
US 20140310220A1 · Chang · 2014 [cited by applicant]
US 20150106315A1 · Birdwell · 2015 [cited by applicant]
US 20150138183A1 · Kishi · 2015 [cited by applicant]
US 20150178619A1 · Nishitani · 2015 [cited by applicant]
US 20150199963A1 · Maaninen · 2015 [cited by applicant]
US 20150213898A1 · Do · 2015 [cited by applicant]
US 20150262055A1 · Akopyan · 2015 [cited by applicant]
US 20150324691A1 · Dropps · 2015 [cited by examiner]
US 20150371711A1 · Choy · 2015 [cited by applicant]
US 20160042790A1 · Tran · 2016 [cited by applicant]
US 20160048755A1 · Freyman · 2016 [cited by applicant]
US 20160093382A1 · Sakamoto · 2016 [cited by applicant]
US 20160133639A1 · Tran · 2016 [cited by applicant]
US 20160180945A1 · Ng · 2016 [cited by applicant]
US 20160254269A1 · Kim et al. · 2016 [cited by applicant]
US 20170330070A1 · Sengupta · 2017 [cited by applicant]
US 20170337466A1 · Bayat · 2017 [cited by applicant]
US 20170337971A1 · Tran · 2017 [cited by applicant]
US 20170337980A1 · Guo · 2017 [cited by applicant]
US 20180004708A1 · Muralimanohar · 2018 [cited by applicant]
US 20180095930A1 · Lu · 2018 [cited by applicant]
US 20180165573A1 · Hsu · 2018 [cited by applicant]
US 20180174034A1 · Obradovic · 2018 [cited by applicant]
US 20180268912A1 · Guo et al. · 2018 [cited by applicant]
US 20180293487A1 · Copel et al. · 2018 [cited by applicant]
US 20190019538A1 · Li · 2019 [cited by applicant]
US 20190019564A1 · Li · 2019 [cited by applicant]
US 20190042199A1 · Sumbul · 2019 [cited by applicant]
US 20190088325A1 · Tiwari et al. · 2019 [cited by applicant]
US 20190088329A1 · Tiwari · 2019 [cited by applicant]
US 20190095779A1 · Leobandung · 2019 [cited by applicant]
US 20190205729A1 · Tran · 2019 [cited by applicant]
US 20190213234A1 · Bayat · 2019 [cited by applicant]
US 20190237136A1 · Tran et al. · 2019 [cited by applicant]
US 20190278675A1 · Bolkhovitin · 2019 [cited by applicant]
US 20190294416A1 · Hu · 2019 [cited by applicant]
US 20200020393A1 · Al-Shamma · 2020 [cited by applicant]
US 20200201751A1 · Wei · 2020 [cited by applicant]
US 20210232893A1 · Bayat · 2021 [cited by applicant]
CN 102567784 · 2012 [cited by applicant]
CN 102906767 · 2013 [cited by applicant]
CN 104615909 · 2015 [cited by applicant]
EP 0566739A1 · 1993 [cited by applicant]
EP 0562737B1 · 1998 [cited by applicant]
JP H03018985A · 1991 [cited by applicant]
JP H0346195A · 1991 [cited by applicant]
JP 63261784 · 1998 [cited by applicant]
JP 2003151290 · 2003 [cited by applicant]
JP 2009080892A · 2009 [cited by applicant]
TW 200414549A · 2004 [cited by applicant]
TW I625843 · 2018 [cited by applicant]
TW 201837759 · 2018 [cited by applicant]
TW 201901285 · 2019 [cited by applicant]
WO 2017131653 · 2017 [cited by applicant]
U.S. Appl. No. 16/382,034 entitled “Neural Network Classifier Using Array of Four-Gate Non-volatile Memory Cells,” Tran, et al., filed Apr. 11, 2019. [cited by applicant]
U.S. Appl. No. 16/382,045 entitled “Neural Network Classifier Using Array of Three-Gate Non-volatile Memory Cells,” Tran, et al., filed Apr. 11, 2019. [cited by applicant]
U.S. Appl. No. 16/382,051 entitled “Neural Network Classifier Using Array of Stacked Gate Non-volatile Memory Cells,” Tran, et al., filed Apr. 11, 2019. [cited by applicant]
U.S. Appl. No. 16/382,060 entitled “Memory Device and Method for Varying Program State Separation Based Upon Frequency of Use,” Tran, et al., filed Apr. 11, 2019. [cited by applicant]
U.S. Appl. No. 15/826,345 entitled “High Precision and Highly Efficient Tuning Mechanisms . . . Network,” Tran, et al., filed Nov. 29, 2017. [cited by applicant]
U.S. Appl. No. 16/271,673 entitled “Flash Memory Array With Individual Memory Cell Read, Program and Erase,” Guo et al., filed Feb. 8, 2019. [cited by applicant]
U.S. Appl. No. 15/991,890 entitled “Decoders for Analog Neural Memory in Deep Learning Artificial Neural Network,” Tran, et al., filed May 29, 2018. [cited by applicant]
U.S. Appl. No. 16/353,830 entitled “System for Converting Neuron Current Into Neuron Current-Based Time Pulses . . . Network,” Tran et al., filed Mar. 14, 2019. [cited by applicant]
U.S. Appl. No. 16/503,355, filed Jul. 3, 2019, Tran et al. [cited by applicant]
U.S. Appl. No. 16/354,04, filed Mar. 14, 2019, Tran et al. [cited by applicant]
U.S. Appl. No. 16/353,409, titled “Apparatus and Method for Combining Analog Neural Net With FPGA Routing in a Monolithic Integrated Circuit,” filed Mar. 14, 2019. [cited by applicant]
U.S. Appl. No. 62/797,158, titled “Apparatus and Method for Combining Analog Neural Net With FPGA Routing in a Monolithic Integrated Circuit,” filed Jan. 25, 2019. [cited by applicant]
U.S. Appl. No. 16/382,013 entitled “Neural Network Classifier Using Array of Two-Gate Non-volatile Memory Cells,” Tran, et al., filed Apr. 11, 2019. [cited by applicant]
Jenn-Chyou Bor, et al., “Realization of the CMOS Pulsewidth-Modulation (PWM) Neural Network with On-Chip Learning,” IEEE Transactions on Circuits and Systems, Jan. 1998. [cited by applicant]
Alister Hamilton, et al., “Integrated Pulse Stream Neural Networks: Results, Issues, and Pointers,” IEEE Transactions on Neural Networks, May 1992. [cited by applicant]
Shafiee et al,“Isaac: A Convolutional Neural Network Accelerator With In-Situ Analog Arithmetic in Crossbars;” 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture, Seoul, Oct. 2016. [URL:https://w… [cited by applicant]
Guo, et al., “Temperature-insensitive Analog Vector-by-Matrix Multiplier Based on 55 nm NOR Flash Memory Cells,” IEEE Custom Integrated Circuits Conference, Apr. 30, 2017, pp. 1-4. [cited by applicant]
Lin, et al., “A Novel Voltage-Accumulation Vector-Matrix Multiplication Architecture Using Resistor-shunted Floating Gate Flash Memory Device for Low-power and High-density Neural Network Applications,” IEEE Internation… [cited by applicant]
Bavandpour, et al., “Energy-Efficient Time-Domain Vector-by-Matrix Multiplier for Neurocomputing and Beyond,” retrieved from the Internet: url:https:/arxiv.org/pdf/1711.10673.pdf, pp. 1-6. [cited by applicant]
Mehrotra, et al., “Elements of Artificial Neural Networks,” 1997 Massachusetts Institute of Technology, pp. 1-351, a Bradford Book, The MIT Press, Cambridge, Mass, London, England. [cited by applicant]
Bayat, “Memory Technologies for Neural Networks,” IEEE, 2015. [cited by applicant]
European Examiner's Report dated Nov. 25, 2021 for European Patent Application No. 17799921.6. [cited by applicant]
Chinese Office Action dated Oct. 9, 2021 for Chinese Patent Application No. 201780030390.9. [cited by applicant]