Mimicking neuroplasticity via ion migration in van der Waals layered copper indium thiophosphate

• Main Authors: Chen, Jiangang, Zhu, Chao, Cao, Guiming, Liu, Haishi, Bian, Renji, Wang, Jinyong, Li, Changcun, Chen, Jieqiong, Fu, Qundong, Liu, Qing, Meng, Peng, Li, Wei, Liu, Fucai, Liu, Zheng
• Other Authors: School of Materials Science and Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Electrical and electronic engineering::Nanoelectronics; Engineering::Materials::Microelectronics and semiconductor materials; Copper Indium Thiophosphate; Ion Migration
• Online Access: https://hdl.handle.net/10356/156536
• Institution: Nanyang Technological University

Artificial synaptic devices are the essential components of neuromorphic computing systems, which are capable of parallel information storage and processing with high area and energy efficiencies, showing high promise in future storage systems and in-memory computing. Analogous to the diffusion of neurotransmitter between neurons, ion-migration-based synaptic devices are becoming promising for mimicking synaptic plasticity, though the precise control of ion migration is still challenging. Due to the unique 2D nature and highly anisotropic ionic transport properties, van der Waals layered materials are attractive for synaptic device applications. Here, utilizing the high conductivity from Cu+ -ion migration, a two-terminal artificial synaptic device based on layered copper indium thiophosphate is studied. By controlling the migration of Cu+ ions with an electric field, the device mimics various neuroplasticity functions, such as short-term plasticity, long-term plasticity, and spike-time-dependent plasticity. The Pavlovian conditioning and activity-dependent synaptic plasticity involved neural functions are also successfully emulated. These results show a promising opportunity to modulate ion migration in 2D materials through field-driven ionic processes, making the demonstrated synaptic device an intriguing candidate for future low-power neuromorphic applications.