Synaptic Plasticity on Demand for Neuromorphic Computing and Bio-Inspired Artificial Nerve
Tae-Woo Lee a
a Seoul National university, 대한민국 서울특별시 관악구 관악로 1, Korea, Republic of
Materials for Sustainable Development Conference (MATSUS)
Proceedings of MATSUS Spring 2024 Conference (MATSUS24)
#OPTMS - Organic and perovskite-based transistor memoristors and synapses
Barcelona, Spain, 2024 March 4th - 8th
Organizers: Jung-Yao Chen, Chu-Chen Chueh and Wen-Ya Lee
Invited Speaker, Tae-Woo Lee, presentation 434
DOI: https://doi.org/10.29363/nanoge.matsus.2024.434
Publication date: 18th December 2023

Biological nervous systems possess versatile attributes, serving diverse functions; for instance, the central nervous system (CNS) governs learning and memory, while the peripheral nervous system (PNS) is responsible for sensory perception. Consequently, there is a need to engineer artificial synapses tailored to adapt to performance requirements in various applications. Brain-inspired neuromorphic computing aims to emulate the learning and memory capabilities of the CNS, being inspired from the long-term potentiation (LTP) observed in biological synapses. The application of artificial synapses in the fields of nervetronics and neuroprosthetics requires the emulation of the short-term plasticity (STP), enabling the rapid signal transmission and fast responses akin to those in the biological PNS. To demonstrate the broad applicability, spanning areas including neuromorphic computing and bio-inspired nervetronics, our study has explored the modulation of STP and LTP using ion-gel gated polymer synaptic transistors (IGPSTs). We have modulated the polymer semiconductor (PSC) film’s crystallinity through post-deposition film annealing, self-assembly monolayer treatment, and the introduction of various sidechain length, leading to the conversion between STP and LTP properties in IGPSTs. Moreover, we have utilized a straightforward yet effective approach by blending two PSCs with the same backbone but different sidechains. This blend strategy has led to a substantial improvement in LTP characteristics, whereas IGPSTs employing each PSC individually show only STP properties. IGPSTs with enhanced LTP properties demonstrate their potential as neuromorphic computing devices, effectively simulating learning processes in artificial neural networks. Concurrently, IGPSTs featuring STP capabilities are used to demonstrate various artificial nervous systems, such as artificial reflex arcs, neuromuscular systems, and neuro-prosthetic nerves incorporating artificial proprioceptors. These pioneering studies on neuromorphic devices have expanded the scope of applications for artificial synapses and validate the feasibility of these innovative technologies.

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