• 로봇학회논문지
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• 제1권2호
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• pp.163-171
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• 2006

This paper introduces the research progress on the artificial brain in the Telerobotics and Control Laboratory at KAIST. This series of studies is based on the assumption that it will be possible to develop an artificial intelligence by copying the mechanisms of the animal brain. Two important brain mechanisms are considered: spike-timing dependent plasticity and dopaminergic plasticity. Each mechanism is implemented in two coding paradigms: spike-codes and rate-codes. Spike-timing dependent plasticity is essential for self-organization in the brain. Dopamine neurons deliver reward signals and modify the synaptic efficacies in order to maximize the predicted reward. This paper addresses how artificial intelligence can emerge by the synergy between self-organization and reinforcement learning. For implementation issues, the rate codes of the brain mechanisms are developed to calculate the neuron dynamics efficiently.

• JSTS:Journal of Semiconductor Technology and Science
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• 제15권6호
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• pp.658-663
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• 2015

In the previous work, we have proposed an integrate-and-fire neuron circuit and synaptic device based on the floating body MOSFET [1-3]. Integrate-and-Fire(I&F) neuron circuit emulates the biological neuron characteristics such as integration, threshold triggering, output generation, refractory period using floating body MOSFET. The synaptic device has short-term and long-term memory in a single silicon device. In this paper, we connect the neuron circuit and the synaptic device using current mirror circuit for summation of post synaptic pulses. We emulate spike-timing-dependent-plasticity (STDP) characteristics of the synapse using feedback voltage without controller or clock. Using memory device in the logic circuit, we can emulate biological synapse and neuron with a small number of devices.

• JSTS:Journal of Semiconductor Technology and Science
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• 제16권5호
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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.

• 센서학회지
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• 제27권1호
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• pp.64-67
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• 2018

A new CMOS neuron circuit for implementing bistable synapses with spike-timing-dependent plasticity (STDP) properties has been proposed. In neuromorphic systems using STDP properties, the short-term dynamics of the synaptic efficacies are governed by the relative timing of the pre- and post-synaptic spikes, and the efficacies tend asymptotically to either a potentiated state or to a depressed one on long time scales. The proposed circuit consists of a negative shifter, a current starved inverter and a schmitt trigger designed using 0.18um CMOS technology. The simulation result shows that the proposed circuit can reduce the total size of neurons, and the spike energy of the proposed circuit is much less compared to the conventional circuits.

• JSTS:Journal of Semiconductor Technology and Science
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• 제17권2호
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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).

• JSTS:Journal of Semiconductor Technology and Science
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• 제17권2호
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• pp.174-179
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• 2017

We designed the CMOS analog integrate and fire (I&F) neuron circuit for driving memristor based on resistive-switching random access memory (RRAM). And we fabricated the RRAM device that have $HfO_2$ switching layer using atomic layer deposition (ALD). The RRAM device has gradual set and reset characteristics. By spice modeling of the synaptic device, we performed circuit simulation of synaptic device and CMOS neuron circuit. The neuron circuit consists of a current mirror for spatial integration, a capacitor for temporal integration, two inverters for pulse generation, a refractory part, and finally a feedback part for learning of the RRAM. We emulated the spike-timing-dependent-plasticity (STDP) characteristic that is performed automatically by pre-synaptic pulse and feedback signal of the neuron circuit. By STDP characteristics, the synaptic weight, conductance of the RRAM, is changed without additional control circuit.

• 전기전자학회논문지
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• 제26권4호
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• pp.633-638
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• 2022

최근 인간의 뇌를 모방한 스파이킹 뉴럴 네트워크(SNNs)의 뉴로모픽(Neuromorphic) 시스템이 주목을 받고 있다. 뉴로모픽 기술은 인지 응용과 처리 과정에서 속도가 빠르고 전력 소모가 적다는 장점이 있다. SNNs 기반의 저항성 랜덤 엑세스 메모리(RRAM) 은 병렬 연산을 위한 가장 효율적인 구조이며 스파이크 타이밍 종속 가소성(STDP)의 점진적인 스위칭 동작을 수행한다. 시냅스 소자 동작으로서의 RRAM은 저 전력 프로세싱과 다양한 메모리 상태를 표현한다. 하지만, RRAM 소자의 통합은 높은 스위칭 전압 및 전류를 유발하여 높은 전력 소비를 초래한다. RRAM의 동작 전압을 낮추기 위해서는 스위칭 레이어와 금속 전극의 신소재를 개발하는 것이 중요하다. 본 연구에서는 스위칭 전압을 낮추기 위해 전기적, 기계적 특성이 우수한 단일 벽 탄소나노튜브(SWCNTs)를 갖는 (Metal/Al₂O₃/HfO_x/SWCNTs/N+silicon, MOCS)라는 최적화된 새로운 구조를 제안하였다. 따라서 SWCNTs 기반 멤리스터의 점진적인 스위칭 동작 및 저 전력 I/V 곡선의 향상을 보여준다.