Intrinsic plasticity of silicon nanowire neurotransistors for dynamic memory and learning functions

被引:0
|
作者
Eunhye Baek
Nikhil Ranjan Das
Carlo Vittorio Cannistraci
Taiuk Rim
Gilbert Santiago Cañón Bermúdez
Khrystyna Nych
Hyeonsu Cho
Kihyun Kim
Chang-Ki Baek
Denys Makarov
Ronald Tetzlaff
Leon Chua
Larysa Baraban
Gianaurelio Cuniberti
机构
[1] TU Dresden,Institute for Materials Science and Max Bergmann Center of Biomaterials
[2] TU Dresden,Center for Advancing Electronics Dresden
[3] Tsinghua University,Center for Brain
[4] University of Calcutta,Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip
[5] TU Dresden,Department of Radio Physics and Electronics
[6] Tsinghua University,Biomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Cluster of Excellence Physics of Life (PoL)
[7] Pohang University of Science and Technology,Center for Complex Network Intelligence (CCNI), Tsinghua Laboratory of Brain and Intelligence
[8] Helmholtz-Zentrum Dresden-Rossendorf e.V.,Department of Creative IT Engineering
[9] Jeonbuk National University,Institute of Ion Beam Physics and Materials Research
[10] Technische Universität Dresden,Division of Electronic Engineering
[11] EECS Department,Chair of Fundamentals of Electrical Engineering
[12] Helmholtz-Zentrum Dresden-Rossendorf e.V.,University of California
来源
Nature Electronics | 2020年 / 3卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Neuromorphic architectures merge learning and memory functions within a single unit cell and in a neuron-like fashion. Research in the field has been mainly focused on the plasticity of artificial synapses. However, the intrinsic plasticity of the neuronal membrane is also important in the implementation of neuromorphic information processing. Here we report a neurotransistor made from a silicon nanowire transistor coated by an ion-doped sol–gel silicate film that can emulate the intrinsic plasticity of the neuronal membrane. The neurotransistors are manufactured using a conventional complementary metal–oxide–semiconductor process on an 8-inch (200 mm) silicon-on-insulator wafer. Mobile ions allow the film to act as a pseudo-gate that generates memory and allows the neurotransistor to display plasticity. We show that multiple pulsed input signals of the neurotransistor are non-linearly processed by sigmoidal transformation into the output current, which resembles the functioning of a neuronal membrane. The output response is governed by the input signal history, which is stored as ionic states within the silicate film, and thereby provides the neurotransistor with learning capabilities.
引用
收藏
页码:398 / 408
页数:10
相关论文
共 50 条
  • [31] Forming a nonlinear grating in Silicon nanowire waveguides using the intrinsic anisotropic Kerr nonlinearity of Silicon
    Driscoll, Jeffrey B.
    Grote, Richard R.
    Liu, Xiaoping
    Dadap, Jerry I.
    Panoiu, Nicolae C.
    Osgood, Richard M., Jr.
    2011 CONFERENCE ON LASERS AND ELECTRO-OPTICS (CLEO), 2011,
  • [32] Dynamic plasticity of macrophage functions in diseased liver
    Ritz, Thomas
    Krenkel, Oliver
    Tacke, Frank
    CELLULAR IMMUNOLOGY, 2018, 330 : 175 - 182
  • [33] Intrinsic resistive switching and memory effects in silicon oxide
    Jun Yao
    Lin Zhong
    Douglas Natelson
    James M. Tour
    Applied Physics A, 2011, 102 : 835 - 839
  • [34] Intrinsic resistive switching and memory effects in silicon oxide
    Yao, Jun
    Zhong, Lin
    Natelson, Douglas
    Tour, James M.
    APPLIED PHYSICS A-MATERIALS SCIENCE & PROCESSING, 2011, 102 (04): : 835 - 839
  • [35] Intrinsic Learning of Dynamic Bayesian Networks
    Black, Alex
    Korb, Kevin B.
    Nicholson, Ann E.
    PRICAI 2014: TRENDS IN ARTIFICIAL INTELLIGENCE, 2014, 8862 : 256 - 269
  • [36] Intrinsic learning of dynamic Bayesian networks
    Black, Alex, 1600, Springer Verlag (8862):
  • [37] Memory traces: Neuronal plasticity in the cortex in relation to learning and memory
    Luiten, PGM
    EUROPEAN JOURNAL OF MORPHOLOGY, 1997, 35 (01): : 37 - 38
  • [38] Single electron memory characteristic of silicon nanodot nanowire transistor
    Tsutsumi, T
    Ishii, K
    Suzuki, E
    Hiroshima, H
    Yamanaka, M
    Sakata, I
    Kanemaru, S
    Hazra, S
    Maeda, T
    Tomizawa, K
    ELECTRONICS LETTERS, 2000, 36 (15) : 1322 - 1323
  • [39] Optimizing Extreme Learning Machine via Generalized Hebbian Learning and Intrinsic Plasticity Learning
    Chao Chen
    Xinyu Jin
    Boyuan Jiang
    Lanjuan Li
    Neural Processing Letters, 2019, 49 : 1593 - 1609
  • [40] Optimizing Extreme Learning Machine via Generalized Hebbian Learning and Intrinsic Plasticity Learning
    Chen, Chao
    Jin, Xinyu
    Jiang, Boyuan
    Li, Lanjuan
    NEURAL PROCESSING LETTERS, 2019, 49 (03) : 1593 - 1609