Solution-Processed Polymer Memcapacitors with Stimulus-Controlled and Evolvable Synaptic Functionalities: From Short-Term Plasticity to Long-Term Plasticity to Metaplasticity
In the vanguard of neuromorphic engineering, we develop a paradigm of biocompatible polymer memcapacitors using a seamless solution process, unleashing comprehensive synaptic capabilities depending on both the stimulation form and history. Like the human brain to learn and adapt, the memcapacitors exhibit analogue-type and evolvable capacitance shifts that mirror the complex flexibility of synaptic strengthening and weakening. With increasing frequency and intensity of the stimulation, the memcapacitors demonstrate an evolution from short-term plasticity (STP) to long-term plasticity (LTP), and even to metaplasticity (MP) at a higher level. A physical picture, featuring the stimulus-controlled spatiotemporal ion redistribution in the polymer, elaborates the origin of the memcapacitive prowess and resultant versatile synaptic plasticity. The distinctive MP behavior endows the memcapacitors with a dynamic learning rate (LR), which is utilized in an artificial neural network. The superiority of implementing a dynamic LR compared with conventional practices of using constant LR shines light on the potential of the memcapacitors to exploit organic neuromorphic computing hardware.
Authors
- Jing-Ting Ye
- Shuit‐Tong Lee (ORCID: https://orcid.org/0000-0003-1238-9802)
- Xu Gao (ORCID: https://orcid.org/0000-0002-7491-0758)
- Zhong‐Da Zhang
- Ya‐Nan Zhong (ORCID: https://orcid.org/0000-0003-0400-1441)
- Sui‐Dong Wang (ORCID: https://orcid.org/0000-0002-4761-4793)
- Jianlong Xu (ORCID: https://orcid.org/0000-0002-0824-657X)
- Xue-Er Han
- Lixing Li
- Hao Zong
- Jiawei Cai (ORCID: https://orcid.org/0009-0005-0558-8952)
Institutions
- Macau University of Science and Technology (MO)
- Soochow University (CN)
Publication Details
- Journal
- ACS Applied Materials & Interfaces
- Published
- 2024-09-02
- DOI
- https://doi.org/10.1021/acsami.4c09593
- Citations
- 11
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.30
Funders
- National Natural Science Foundation of China
- Collaborative Innovation Center of Suzhou Nano Science and Technology
- Higher Education Discipline Innovation Project