Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Korea.
SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16417, Korea.
Nat Commun. 2018 Nov 30;9(1):5106. doi: 10.1038/s41467-018-07572-5.
The priority of synaptic device researches has been given to prove the device potential for the emulation of synaptic dynamics and not to functionalize further synaptic devices for more complex learning. Here, we demonstrate an optic-neural synaptic device by implementing synaptic and optical-sensing functions together on h-BN/WSe heterostructure. This device mimics the colored and color-mixed pattern recognition capabilities of the human vision system when arranged in an optic-neural network. Our synaptic device demonstrates a close to linear weight update trajectory while providing a large number of stable conduction states with less than 1% variation per state. The device operates with low voltage spikes of 0.3 V and consumes only 66 fJ per spike. This consequently facilitates the demonstration of accurate and energy efficient colored and color-mixed pattern recognition. The work will be an important step toward neural networks that comprise neural sensing and training functions for more complex pattern recognition.
突触器件的研究重点一直放在证明器件在模拟突触动力学方面的潜力上，而不是进一步使突触器件功能化以实现更复杂的学习。在这里，我们通过在 h-BN/WSe 异质结构上共同实现突触和光传感功能，展示了一种光电神经突触器件。当该器件排列在光神经网络中时，它模拟了人类视觉系统的彩色和混色模式识别能力。我们的突触器件在提供大量稳定导通状态的同时，其权重更新轨迹接近线性，每个状态的变化小于 1%。该器件的工作电压尖峰低至 0.3V，每个尖峰仅消耗 66fJ 的能量。这使得准确和节能的彩色和混色模式识别的演示成为可能。这项工作将是朝着包含神经传感和训练功能以实现更复杂模式识别的神经网络迈出的重要一步。
