BCM Learning Rules Emulated by a-IGZO-Based Photoelectronic Neuromorphic Transistors
Shuo Ke, Chuanyu Fu, Xinhuang Lin, Yixin Zhu, Huiwu Mao, Li Zhu, Xiangjing Wang, Chunsheng Chen, Changjin Wan, Qing Wan
Abstract
Hardware implementation of Bienenstock–Cooper–Munro (BCM) learning rules would be of great implications toward artificial intelligent systems. In this work, amorphous indium gallium zinc oxide (a-IGZO)-based photoelectronic neuromorphic transistors were proposed for mimicking BCM learning rules. A SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> electrolyte film with a large electric-double-layer capacitance ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.33~\mu \text{F}$ </tex-math></inline-formula> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) was used for the gate dielectric film. Light induced short-term synaptic plasticity can be mimicked by such device, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and high-pass temporal filtering. More importantly, BCM learning rules are realized based on this photoelectronic neuromorphic transistor. These results would provide a step forward the development of photoelectronic neuromorphic systems with sophisticated learning rules.