DE112018000134T5 - Memristive Einheit auf Grundlage einer Alkali-Dotierung von Übergangsmetalloxiden - Google Patents

Memristive Einheit auf Grundlage einer Alkali-Dotierung von Übergangsmetalloxiden Download PDF

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Publication number
DE112018000134T5
DE112018000134T5 DE112018000134.2T DE112018000134T DE112018000134T5 DE 112018000134 T5 DE112018000134 T5 DE 112018000134T5 DE 112018000134 T DE112018000134 T DE 112018000134T DE 112018000134 T5 DE112018000134 T5 DE 112018000134T5
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material layer
layer
oxide
conductive
conductive material
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English (en)
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Simcha Gershon Talia
Wayne Brew Kevin
Saurabh Singh
Dennis Newns
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Adeia Semiconductor Solutions LLC
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International Business Machines Corp
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    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N—ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N70/00—Solid-state devices having no potential barriers, and specially adapted for rectifying, amplifying, oscillating or switching
    • H10N70/20—Multistable switching devices, e.g. memristors
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N—ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N70/00—Solid-state devices having no potential barriers, and specially adapted for rectifying, amplifying, oscillating or switching
    • H10N70/20—Multistable switching devices, e.g. memristors
    • H10N70/24—Multistable switching devices, e.g. memristors based on migration or redistribution of ionic species, e.g. anions, vacancies
    • H10N70/245—Multistable switching devices, e.g. memristors based on migration or redistribution of ionic species, e.g. anions, vacancies the species being metal cations, e.g. programmable metallization cells
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0499—Feedforward networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/065—Analogue means
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N—ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N70/00—Solid-state devices having no potential barriers, and specially adapted for rectifying, amplifying, oscillating or switching
    • H10N70/011—Manufacture or treatment of multistable switching devices
    • H10N70/021—Formation of switching materials, e.g. deposition of layers
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N—ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N70/00—Solid-state devices having no potential barriers, and specially adapted for rectifying, amplifying, oscillating or switching
    • H10N70/011—Manufacture or treatment of multistable switching devices
    • H10N70/041—Modification of switching materials after formation, e.g. doping
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N—ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N70/00—Solid-state devices having no potential barriers, and specially adapted for rectifying, amplifying, oscillating or switching
    • H10N70/801—Constructional details of multistable switching devices
    • H10N70/821—Device geometry
    • H10N70/826—Device geometry adapted for essentially vertical current flow, e.g. sandwich or pillar type devices
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N—ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N70/00—Solid-state devices having no potential barriers, and specially adapted for rectifying, amplifying, oscillating or switching
    • H10N70/801—Constructional details of multistable switching devices
    • H10N70/821—Device geometry
    • H10N70/828—Current flow limiting means within the switching material region, e.g. constrictions
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N—ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N70/00—Solid-state devices having no potential barriers, and specially adapted for rectifying, amplifying, oscillating or switching
    • H10N70/801—Constructional details of multistable switching devices
    • H10N70/841—Electrodes
    • H10N70/8416—Electrodes adapted for supplying ionic species
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N—ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10N70/00—Solid-state devices having no potential barriers, and specially adapted for rectifying, amplifying, oscillating or switching
    • H10N70/801—Constructional details of multistable switching devices
    • H10N70/881—Switching materials
    • H10N70/883—Oxides or nitrides
    • H10N70/8833—Binary metal oxides, e.g. TaOx

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Computational Linguistics (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Manufacturing & Machinery (AREA)
  • Neurology (AREA)
  • Semiconductor Memories (AREA)
  • Thyristors (AREA)
DE112018000134.2T 2017-01-13 2018-01-03 Memristive Einheit auf Grundlage einer Alkali-Dotierung von Übergangsmetalloxiden Pending DE112018000134T5 (de)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US15/405,555 US10164179B2 (en) 2017-01-13 2017-01-13 Memristive device based on alkali-doping of transitional metal oxides
US15/405,555 2017-01-13
PCT/IB2018/050033 WO2018130914A1 (en) 2017-01-13 2018-01-03 Memristive device based on alkali-doping of transitional metal oxides

Publications (1)

Publication Number Publication Date
DE112018000134T5 true DE112018000134T5 (de) 2019-07-04

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Country Link
US (1) US10164179B2 (enExample)
JP (1) JP6921961B2 (enExample)
CN (1) CN110168761A (enExample)
DE (1) DE112018000134T5 (enExample)
GB (1) GB2573693A (enExample)
WO (1) WO2018130914A1 (enExample)

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017222592A1 (en) 2016-06-20 2017-12-28 Massachusetts Institute Of Technology Apparatus and methods for electrical switching
US10127494B1 (en) * 2017-08-02 2018-11-13 Google Llc Neural network crossbar stack
US11455521B2 (en) 2019-03-01 2022-09-27 International Business Machines Corporation Neuromorphic device driven by copper ion intercalation
US11079958B2 (en) 2019-04-12 2021-08-03 Intel Corporation Apparatus, system and method for offloading data transfer operations between source and destination storage devices to a hardware accelerator
US11250315B2 (en) 2019-10-29 2022-02-15 International Business Machines Corporation Electrochemical device of variable electrical conductance
US11552246B2 (en) 2020-01-21 2023-01-10 Massachusetts Institute Of Technology Memristors and related systems and methods
US11742901B2 (en) * 2020-07-27 2023-08-29 Electronics And Telecommunications Research Institute Deep learning based beamforming method and apparatus
US11397544B2 (en) 2020-11-10 2022-07-26 International Business Machines Corporation Multi-terminal neuromorphic device
US11361821B2 (en) * 2020-11-10 2022-06-14 International Business Machines Corporation Drift and noise corrected memristive device
US11615842B2 (en) 2020-12-14 2023-03-28 International Business Machines Corporation Mixed conducting volatile memory element for accelerated writing of nonvolatile memristive device
CN113035953B (zh) * 2021-02-08 2023-07-14 清华大学 一种无机耐高温突触晶体管及其制备方法
JP2022125660A (ja) 2021-02-17 2022-08-29 キオクシア株式会社 記憶装置及び記憶方法

Family Cites Families (29)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3010685B2 (ja) * 1990-05-28 2000-02-21 ソニー株式会社 有機電解質電池
US5908715A (en) * 1997-05-30 1999-06-01 Hughes Electronics Corporation Composite carbon materials for lithium ion batteries, and method of producing same
KR100860134B1 (ko) * 2001-08-13 2008-09-25 어드밴스드 마이크로 디바이시즈, 인코포레이티드 메모리 셀
KR100593448B1 (ko) * 2004-09-10 2006-06-28 삼성전자주식회사 전이금속 산화막을 데이터 저장 물질막으로 채택하는비휘발성 기억 셀들 및 그 제조방법들
KR20090125256A (ko) * 2007-03-26 2009-12-04 사임베트 코퍼레이션 리튬 막박 전지용 기재
US20080314738A1 (en) * 2007-06-19 2008-12-25 International Business Machines Corporation Electrolytic Device Based on a Solution-Processed Electrolyte
CN101315969A (zh) 2008-06-26 2008-12-03 复旦大学 一种具有掺杂控制层的电阻存储器
US20110121359A1 (en) * 2008-07-31 2011-05-26 Jianhua Yang Multi-Layer Reconfigurable Switches
KR101502898B1 (ko) * 2008-11-10 2015-03-25 삼성에스디아이 주식회사 리튬 이차 전지용 복합 음극 활물질, 이의 제조 방법 및 이를 구비한 리튬 이차 전지
US8614432B2 (en) 2009-01-15 2013-12-24 Hewlett-Packard Development Company, L.P. Crystalline silicon-based memristive device with multiple mobile dopant species
KR101105981B1 (ko) * 2009-04-28 2012-01-18 한양대학교 산학협력단 저항변화 메모리 소자 및 이의 제조방법
CN102484127B (zh) 2009-09-04 2015-07-15 惠普开发有限公司 基于混合金属价键化合物的记忆电阻
US8120071B2 (en) 2010-01-11 2012-02-21 Hewlett-Packard Development Company, L.P. Memfet ram
WO2011133158A1 (en) 2010-04-22 2011-10-27 Hewlett-Packard Development Company, L.P. Switchable two-terminal devices with diffusion/drift species
CN102244193A (zh) 2010-05-13 2011-11-16 复旦大学 包含钌掺杂的氧化钽基电阻型存储器及其制备方法
EP2641331B1 (en) * 2010-11-19 2020-06-03 Hewlett-Packard Enterprise Development LP Method and circuit for switching a memristive device
US20140184380A1 (en) * 2010-11-26 2014-07-03 Varun Aggarwal Multi-state memory resistor device and methods for making thereof
FR2969382B1 (fr) * 2010-12-17 2022-11-18 Centre Nat Rech Scient Élément memristif et mémoire électronique basée sur de tels éléments
CN102610746A (zh) * 2011-01-20 2012-07-25 中国科学院微电子研究所 非挥发性电阻转变存储器
WO2013003978A1 (zh) 2011-07-06 2013-01-10 复旦大学 包含钌掺杂的氧化钽基电阻型存储器及其制备方法
JP6180700B2 (ja) * 2011-09-09 2017-08-16 ルネサスエレクトロニクス株式会社 不揮発性半導体記憶装置及びその製造方法
WO2013134757A1 (en) * 2012-03-09 2013-09-12 Privatran, Inc. Memristive device and method of manufacture
US9224461B2 (en) * 2012-11-27 2015-12-29 Intel Corporation Low voltage embedded memory having cationic-based conductive oxide element
US20140175371A1 (en) * 2012-12-21 2014-06-26 Elijah V. Karpov Vertical cross-point embedded memory architecture for metal-conductive oxide-metal (mcom) memory elements
GB2516841A (en) 2013-07-31 2015-02-11 Ibm Resistive memory element based on oxygen-doped amorphous carbon
CN104752608A (zh) 2013-12-26 2015-07-01 北京有色金属研究总院 一种忆阻器及其制备方法
DE102014113030A1 (de) * 2014-09-10 2016-03-10 Infineon Technologies Ag Speicherschaltungen und ein Verfahren zum Bilden einer Speicherschaltung
TWI560918B (en) * 2014-10-15 2016-12-01 Univ Nat Sun Yat Sen Resistance random access memory
CN104916777B (zh) 2015-05-08 2017-11-03 浙江大学 一种锂离子掺杂石墨烯忆阻器及其制备方法

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
C. Lehmann et al. mit dem Titel „A Generic Systolic Array Building Block For Neural Networks with On-Chip Learning", IEEE Transactions On Neural Networks, Band 4, Nr. 3, Mai 1993
Chua, L. O. mit dem Titel „Resistance Switching Memories are Memristors", Applied Physics A (2011), 102 (4): 765 bis 783
D. Soudry et al. mit dem Titel „Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training", IEEE Transactions On Neural Networks and Learning Systems (2015)

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JP6921961B2 (ja) 2021-08-18
US10164179B2 (en) 2018-12-25
JP2020509574A (ja) 2020-03-26
GB201910618D0 (en) 2019-09-11
US20180205011A1 (en) 2018-07-19
GB2573693A (en) 2019-11-13
WO2018130914A1 (en) 2018-07-19
CN110168761A (zh) 2019-08-23

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