Synaptic Transistors Using Backbone-Engineered D-A Conjugated Polymers for Real-Time Wearable Motion Cognition
- Authors
- Wu, Ning; Yu, Qianbo; Liu, Yi; Xu, Zhipeng; Li, Yue; Lee, Hwa Sung; Xu, Wentao
- Issue Date
- Jul-2025
- Publisher
- AMER CHEMICAL SOC
- Keywords
- synaptic transistors; conjugated polymers; backbone engineering; mobility; real-time motioncognition
- Citation
- ACS APPLIED MATERIALS & INTERFACES, v.17, no.30, pp 43316 - 43326
- Pages
- 11
- Indexed
- SCIE
SCOPUS
- Journal Title
- ACS APPLIED MATERIALS & INTERFACES
- Volume
- 17
- Number
- 30
- Start Page
- 43316
- End Page
- 43326
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/126252
- DOI
- 10.1021/acsami.5c03513
- ISSN
- 1944-8244
1944-8252
- Abstract
- Organic neuromorphic electronics using conjugated polymers as an active layer attract a lot of attention, for that their synaptic plasticity can be easily tuned by tailoring of the molecular chain structures. Herein, we synthesize a series of conjugated polymers based on a backbone engineering strategy, using thiophene (T), selenophene (Se), bithiophene (BT) and terthiophene (TT) as donors and diketopyrrolopyrrole (DPP) as acceptor, i.e., PTDPP-T, PTDPP-Se, PTDPP-BT and PTDPP-TT, and used these conjugated polymers to fabricate thin-film synaptic transistors. We investigated the correlation between chemical structures, aggregation states, film morphology, mobility and synaptic plasticity. When BT was used as the donor, the conjugated polymer exhibited the strongest preaggregation, formed a nanowire-structured crystal morphology, and had the appropriate monomer conjugation length, resulting in the highest field-effect mobility similar to 1.33 cm2 V-1 s-1. PTDPP-BT synaptic transistor showed the most favorable synaptic plasticity, in terms of response amplitude, plasticity regulation, and high-pass filtering, and applied to image processing and associated learning. The device was also used for wearable applications and successfully demonstrated for real-time wearable motion cognition, which provides an approach for the development of future neuromorphic devices.
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