• Title, Summary, Keyword: neuromorphic system

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뉴로모픽 시스템용 시냅스 트랜지스터의 최근 연구 동향

  • Nam, Jae-Hyeon;Jang, Hye-Yeon;Kim, Tae-Hyeon;Jo, Byeong-Jin
    • Ceramist
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    • v.21 no.2
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    • pp.4-18
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    • 2018
  • Lastly, neuromorphic computing chip has been extensively studied as the technology that directly mimics efficient calculation algorithm of human brain, enabling a next-generation intelligent hardware system with high speed and low power consumption. Three-terminal based synaptic transistor has relatively low integration density compared to the two-terminal type memristor, while its power consumption can be realized as being so low and its spike plasticity from synapse can be reliably implemented. Also, the strong electrical interaction between two or more synaptic spikes offers the advantage of more precise control of synaptic weights. In this review paper, the results of synaptic transistor mimicking synaptic behavior of the brain are classified according to the channel material, in order of silicon, organic semiconductor, oxide semiconductor, 1D CNT(carbon nanotube) and 2D van der Waals atomic layer present. At the same time, key technologies related to dielectrics and electrolytes introduced to express hysteresis and plasticity are discussed. In addition, we compared the essential electrical characteristics (EPSC, IPSC, PPF, STM, LTM, and STDP) required to implement synaptic transistors in common and the power consumption required for unit synapse operation. Generally, synaptic devices should be integrated with other peripheral circuits such as neurons. Demonstration of this neuromorphic system level needs the linearity of synapse resistance change, the symmetry between potentiation and depression, and multi-level resistance states. Finally, in order to be used as a practical neuromorphic applications, the long-term stability and reliability of the synapse device have to be essentially secured through the retention and the endurance cycling test related to the long-term memory characteristics.

Implementation of Neuromorphic System with Si-based Floating-body Synaptic Transistors

  • Park, Jungjin;Kim, Hyungjin;Kwon, Min-Woo;Hwang, Sungmin;Baek, Myung-Hyun;Lee, Jeong-Jun;Jang, Taejin;Park, Byung-Gook
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.17 no.2
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    • pp.210-215
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    • 2017
  • We have developed the neuromorphic system that can work with the four-terminal Si-based synaptic devices and verified the operation of the system using simulation tool and printed-circuit-board (PCB). The symmetrical current mirrors connected to the n-channel and p-channel synaptic devices constitute the synaptic integration part to express the excitation and the inhibition mechanism of neurons, respectively. The number and the weight of the synaptic devices affect the amount of the current reproduced from the current mirror. The double-stage inverters controlling delay time and the NMOS with large threshold voltage ($V_T$) constitute the action-potential generation part. The generated action-potential is transmitted to next neuron and simultaneously returned to the back gate of the synaptic device for changing its weight based on spike-timing-dependent-plasticity (STDP).

Simulation Study on Silicon-Based Floating Body Synaptic Transistor with Short- and Long-Term Memory Functions and Its Spike Timing-Dependent Plasticity

  • Kim, Hyungjin;Cho, Seongjae;Sun, Min-Chul;Park, Jungjin;Hwang, Sungmin;Park, Byung-Gook
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.16 no.5
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    • pp.657-663
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    • 2016
  • In this work, a novel silicon (Si) based floating body synaptic transistor (SFST) is studied to mimic the transition from short-term memory to long-term one in the biological system. The structure of the proposed SFST is based on an n-type metal-oxide-semiconductor field-effect transistor (MOSFET) with floating body and charge storage layer which provide the functions of short- and long-term memories, respectively. It has very similar characteristics with those of the biological memory system in the sense that the transition between short- and long-term memories is performed by the repetitive learning. Spike timing-dependent plasticity (STDP) characteristics are closely investigated for the SFST device. It has been found from the simulation results that the connectivity between pre- and post-synaptic neurons has strong dependence on the relative spike timing among electrical signals. In addition, the neuromorphic system having direct connection between the SFST devices and neuron circuits are designed.

Design of Lightweight Artificial Intelligence System for Multimodal Signal Processing (멀티모달 신호처리를 위한 경량 인공지능 시스템 설계)

  • Kim, Byung-Soo;Lee, Jea-Hack;Hwang, Tae-Ho;Kim, Dong-Sun
    • The Journal of the Korea institute of electronic communication sciences
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    • v.13 no.5
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    • pp.1037-1042
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    • 2018
  • The neuromorphic technology has been researched for decades, which learns and processes the information by imitating the human brain. The hardware implementations of neuromorphic systems are configured with highly parallel processing structures and a number of simple computational units. It can achieve high processing speed, low power consumption, and low hardware complexity. Recently, the interests of the neuromorphic technology for low power and small embedded systems have been increasing rapidly. To implement low-complexity hardware, it is necessary to reduce input data dimension without accuracy loss. This paper proposed a low-complexity artificial intelligent engine which consists of parallel neuron engines and a feature extractor. A artificial intelligent engine has a number of neuron engines and its controller to process multimodal sensor data. We verified the performance of the proposed neuron engine including the designed artificial intelligent engines, the feature extractor, and a Micro Controller Unit(MCU).

Ultra-Low Powered CNT Synaptic Transistor Utilizing Double PI:PCBM Dielectric Layers (더블 PI:PCBM 유전체 층 기반의 초 저전력 CNT 시냅틱 트랜지스터)

  • Kim, Yonghun;Cho, Byungjin
    • Korean Journal of Materials Research
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    • v.27 no.11
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    • pp.590-596
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    • 2017
  • We demonstrated a CNT synaptic transistor by integrating 6,6-phenyl-C61 butyric acid methyl ester(PCBM) molecules as charge storage molecules in a polyimide(PI) dielectric layer with carbon nanotubes(CNTs) for the transistor channel. Specifically, we fabricated and compared three different kinds of CNT-based synaptic transistors: a control device with $Al_2O_3/PI$, a single PCBM device with $Al_2O_3/PI:PCBM$(0.1 wt%), and a double PCBM device with $Al_2O_3/PI:PCBM$(0.1 wt%)/PI:PCBM(0.05 wt%). Statistically, essential device parameters such as Off and On currents, On/Off ratio, device yield, and long-term retention stability for the three kinds of transistor devices were extracted and compared. Notably, the double PCBM device exhibited the most excellent memory transistor behavior. Pulse response properties with postsynaptic dynamic current were also evaluated. Among all of the testing devices, double PCBM device consumed such low power for stand-by and its peak current ratio was so large that the postsynaptic current was also reliably and repeatedly generated. Postsynaptic hole currents through the CNT channel can be generated by electrons trapped in the PCBM molecules and last for a relatively short time(~ hundreds of msec). Under one certain testing configuration, the electrons trapped in the PCBM can also be preserved in a nonvolatile manner for a long-term period. Its integrated platform with extremely low stand-by power should pave a promising road toward next-generation neuromorphic systems, which would emulate the brain power of 20 W.

Neuron Circuit Using a Thyristor and Inter-neuron Connection with Synaptic Devices

  • Ranjan, Rajeev;Kwon, Min-Woo;Park, Jungjin;Kim, Hyungjin;Park, Byung-Gook
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.15 no.3
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    • pp.365-373
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    • 2015
  • We propose a simple and compact thyristor-based neuron circuit. The thyristor exhibits bi-stable characteristics that can mimic the action potential of the biological neuron, when it is switched between its OFF-state and ON-state with the help of assist circuit. In addition, a method of inter-neuron connection with synaptic devices is proposed, using double current mirror circuit. The circuit utilizes both short-term and long-term plasticity of the synaptic devices by flowing current through them and transferring it to the post-synaptic neuron. The double current mirror circuit is capable of shielding the pre-synaptic neuron from the post synaptic-neuron while transferring the signal through it, maintaining the synaptic conductance unaffected by the change in the input voltage of the post-synaptic neuron.