Dielectric-Engineered High-Speed, Low-Power, Highly Reliable Charge Trap Flash-Based Synaptic Device for Neuromorphic Computing beyond Inference.

The coming of the big-data era brought a need for power-efficient computing that cannot be realized in the Von Neumann architecture. Neuromorphic computing which is motivated by the human brain can greatly reduce power consumption through matrix multiplication, and a device that mimics a human synapse plays an important role. However, many synaptic devices suffer from limited linearity and symmetry without using incremental step pulse programming (ISPP). In this work, we demonstrated a charge-trap flash (CTF)-based synaptic transistor using trap-level engineered Al2O3/Ta2O5/Al2O3 gate stack for successful neuromorphic computing. This novel gate stack provided precise control of the conductance with more than 6 bits. We chose the appropriate bias for highly linear and symmetric modulation of conductance and realized it with very short (25 ns) identical pulses at low voltage, resulting in low power consumption and high reliability. Finally, we achieved high learning accuracy in the training of 60000 MNIST images.

[1]  X. Liang,et al.  Carbon Nanotube-Based Flexible Ferroelectric Synaptic Transistors for Neuromorphic Computing. , 2022, ACS applied materials & interfaces.

[2]  Sungho Kim,et al.  Analog–digital hybrid computing with SnS2 memtransistor for low-powered sensor fusion , 2022, Nature Communications.

[3]  Jaehoon Han,et al.  Energy-Efficient III-V Tunnel FET-Based Synaptic Device with Enhanced Charge Trapping Ability Utilizing Both Hot Hole and Hot Electron Injections for Analog Neuromorphic Computing. , 2022, ACS applied materials & interfaces.

[4]  Youn Sang Kim,et al.  Implementation of Synaptic Device Using Ultraviolet Ozone Treated Water‐in‐Bisalt/Polymer Electrolyte‐Gated Transistor , 2021, Advanced Functional Materials.

[5]  Yu‐Rim Jeon,et al.  Analog Synaptic Transistor with Al-Doped HfO2 Ferroelectric Thin Film. , 2021, ACS applied materials & interfaces.

[6]  Zhongrui Wang,et al.  In-sensor reservoir computing for language learning via two-dimensional memristors , 2021, Science Advances.

[7]  Yang‐Kyu Choi,et al.  All‐Solid‐State Ion Synaptic Transistor for Wafer‐Scale Integration with Electrolyte of a Nanoscale Thickness , 2021, Advanced Functional Materials.

[8]  O. Heinonen,et al.  Metal–insulator transition tuned by oxygen vacancy migration across TiO2/VO2 interface , 2020, Scientific Reports.

[9]  Qing Wu,et al.  A memristor-based hybrid analog-digital computing platform for mobile robotics , 2020, Science Robotics.

[10]  Debarghya Sarkar,et al.  Engineering Complex Synaptic Behaviors in a Single Device: Emulating Consolidation of Short-term Memory to Long-term Memory in Artificial Synapses via Dielectric Band Engineering. , 2020, Nano letters.

[11]  Byung-Gook Park,et al.  Field Effect Transistor-Type Devices Using High-κ Gate Insulator Stacks for Neuromorphic Applications , 2020 .

[12]  Jaehoon Han,et al.  3D Stackable Synaptic Transistor for 3D Integrated Artificial Neural Networks. , 2020, ACS applied materials & interfaces.

[13]  Sungjun Kim,et al.  Effects of Gibbs free energy difference and oxygen vacancies distribution in a bilayer ZnO/ZrO2 structure for applications to bipolar resistive switching , 2019 .

[14]  Yan Wang,et al.  Recent Advances in Transistor‐Based Artificial Synapses , 2019, Advanced Functional Materials.

[15]  J. Yang,et al.  Memristive crossbar arrays for brain-inspired computing , 2019, Nature Materials.

[16]  Jang‐Sik Lee,et al.  Ferroelectric Analog Synaptic Transistors. , 2019, Nano letters.

[17]  Byung Chul Jang,et al.  A Recoverable Synapse Device Using a Three‐Dimensional Silicon Transistor , 2018, Advanced Functional Materials.

[18]  Young Sun,et al.  All‐Solid‐State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing , 2018, Advanced Functional Materials.

[19]  Hyunsang Hwang,et al.  Reliable Multivalued Conductance States in TaO x Memristors through Oxygen Plasma-Assisted Electrode Deposition with in Situ-Biased Conductance State Transmission Electron Microscopy Analysis. , 2018, ACS applied materials & interfaces.

[20]  Y. Roizin,et al.  Charge Transport and the Nature of Traps in Oxygen Deficient Tantalum Oxide. , 2018, ACS applied materials & interfaces.

[21]  Mohammed Affan Zidan,et al.  Reservoir computing using dynamic memristors for temporal information processing , 2017, Nature Communications.

[22]  Sung-Jin Choi,et al.  Carbon Nanotube Synaptic Transistor Network for Pattern Recognition. , 2015, ACS applied materials & interfaces.

[23]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[24]  Jun Yeong Seok,et al.  Highly Uniform, Electroforming‐Free, and Self‐Rectifying Resistive Memory in the Pt/Ta2O5/HfO2‐x/TiN Structure , 2014 .

[25]  Kinam Kim,et al.  In situ observation of filamentary conducting channels in an asymmetric Ta2O5−x/TaO2−x bilayer structure , 2013, Nature Communications.

[26]  Byoungil Lee,et al.  Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. , 2012, Nano letters.

[27]  Kinam Kim,et al.  A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O(5-x)/TaO(2-x) bilayer structures. , 2011, Nature materials.

[28]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

[29]  Carver A. Mead,et al.  Neuromorphic electronic systems , 1990, Proc. IEEE.

[30]  T. Unagami,et al.  Electron trapping levels in rf‐sputtered Ta2O5 films , 1983 .

[31]  Wei Lu,et al.  The future of electronics based on memristive systems , 2018, Nature Electronics.

[32]  Andrea L. Lacaita,et al.  Phase change memories: State-of-the-art, challenges and perspectives , 2005 .