Photoactive Electro-Controlled Visual Perception Memory for Emulating Synaptic Metaplasticity and Hebbian Learning

Meng Yu Tsai^1,2*, Ko Chun Lee^1,2, Che Yi Lin², Yuan Ming Chang², Kenji Watanabe³, Takashi Taniguchi⁴, Ching Hwa Ho⁵, Chen Hsin Lien¹, Po Wen Chiu¹, Yen Fu Lin^2,6,7

¹Institute of Electronics Engineering, National Tsing Hua University, Hsinchu, Taiwan
²Department of Physics, National Chung Hsing University, Taichung, Taiwan
³Research Center for Functional Materials, National Institute for Materials Science, Namiki, Tsukuba, Japan
⁴International Center for Materials Nanoarchitectonics, National Institute for Materials Science, Namiki, Tsukuba, Japan
⁵Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei, Taiwan
⁶Institute of Nanoscience, National Chung Hsing University, Taichung, Taiwan
⁷i-Center for Advanced Science and Technology (i-CAST), National Chung Hsing University, Taichung, Taiwan

* Presenter:Meng Yu Tsai, email:flyy6249@gmail.com

In bionic technology, it has become an innovative process imitating the functionality and structuralism of human biological systems to exploit advanced artificial intelligent machines. Bionics plays a significant role in environmental protection, especially for its low energy loss. By fusing the concept of receptor-like sensing component and synapse-like memory, the photoactive electro-controlled optical sensory memory (PE-SM) is proposed and realized in a single device, which endows a simple methodology of reducing power consumption by photoactive electro-control. The PE-SM is the system built with the stacked atomically thick materials, in which rhenium diselenide serves as a robust photosensor, hexagonal boron nitride serves as a tunneling dielectric, and graphene serves as a charge-storage layer. With the features of the PE-SM, it performs synaptic metaplasticities under optical spikes. In addition, a simulated spiking neural network composed of 24 × 24 PE-SMs is further presented in an unsupervised machine learning environment, performing image recognition via the Hebbian rule. The PE-SM not only improves the neuromorphic computing efficiency but also simplifies the circuit-size structure. Eventually, the concept of photoactive electro-control can extend to other photosensitive 2D materials and provide a new approach of constructing either visual perception memory or photonic synaptic devices.

Keywords: Hebbian learning process, image recognition, optical synapses, photoactive electro-control, rhenium diselenide