Blankenship Brian W, Li Runxuan, Guo Ruihan, Zhao Naichen, Shin Jaeho, Yang Rundi, Ko Seung Hwan, Wu Junqiao, Rho Yoonsoo, Grigoropoulos Costas
Laser Thermal Laboratory, Department of Mechanical Engineering, University of California, Berkeley, California 94720, United States.
Department of Materials Science and Engineering, University of California, Berkeley, California 94720, United States.
Nano Lett. 2023 Oct 11;23(19):9020-9025. doi: 10.1021/acs.nanolett.3c02681. Epub 2023 Sep 19.
Biological nervous systems rely on the coordination of billions of neurons with complex, dynamic connectivity to enable the ability to process information and form memories. In turn, artificial intelligence and neuromorphic computing platforms have sought to mimic biological cognition through software-based neural networks and hardware demonstrations utilizing memristive circuitry with fixed dynamics. To incorporate the advantages of tunable dynamic software implementations of neural networks into hardware, we develop a proof-of-concept artificial synapse with adaptable resistivity. This synapse leverages the photothermally induced local phase transition of VO thin films by temporally modulated laser pulses. Such a process quickly modifies the conductivity of the film site-selectively by a factor of 500 to "activate" these neurons and store "memory" by applying varying bias voltages to induce self-sustained Joule heating between electrodes after activation with a laser. These synapses are demonstrated to undergo a complete heating and cooling cycle in less than 120 ns.
生物神经系统依靠数十亿具有复杂动态连接性的神经元之间的协调，来实现处理信息和形成记忆的能力。反过来，人工智能和神经形态计算平台试图通过基于软件的神经网络以及利用具有固定动态特性的忆阻电路的硬件演示来模仿生物认知。为了将神经网络的可调动态软件实现的优势整合到硬件中，我们开发了一种具有适应性电阻率的概念验证人工突触。这种突触利用时间调制激光脉冲对VO薄膜进行光热诱导局部相变。这样的过程通过将薄膜的电导率快速地选择性改变500倍来“激活”这些神经元，并在激光激活后通过施加不同的偏置电压以诱导电极之间的自维持焦耳热来存储“记忆”。这些突触被证明能在不到120纳秒的时间内经历一个完整的加热和冷却循环。
