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Title: Solution-processable anionic doped conjugated polymer for nonvolatile organic transistor memory with synaptic behaviors
Authors: 張葶葶
Chang, Ting-Ting
Yu, Ting-Feng
Chen, Hao-Yang
Liao, Ming-Yun
Tien, Hsin-Chiao;Chueh, Chu-Chen;Lee, Wen-Ya
Contributors: 心理系
Keywords: synaptic transistors; transistor memories; anionic doping; conjugated polymers; neuromorphic engineering; solution-processing; donor−acceptor copolymers
Date: 2020-07
Issue Date: 2021-01-27 15:21:19 (UTC+8)
Abstract: Brain-inspired synaptic transistors have been considered as a promising device for next-generation electronics. To mimic the behavior of a biological synapse, both data processing and nonvolatile memory capability are simultaneously required for a single electronic device. In this work, a simple approach to realize a synaptic transistor with improved memory characteristics is demonstrated by doping an ionic additive, tetrabutylammonium perchlorate (TBAP), into an active polymer semiconductor without using any extra charge storage layer. TBAP doping is first revealed to improve the memory window of a derived transistor memory device from 19 to 32 V (∼68% enhancement) with an on/off current ratio over 103 at VG = −10 V. Through morphological analysis and theoretical calculations, it is revealed that the association of anion with polymers enhances the charge retention capability of the polymer and facilitates the interchain interactions to result in improved memory characteristics. More critically, the doped device is shown to successfully mimic the synaptic behaviors, such as paired-pulse facilitation (PPF), excitatory and inhibitory postsynaptic currents, and spike-rate dependent plasticity. Notably, the TBAP-doped device is shown to deliver a PPF index of up to 204% in contrast to the negligible value of an undoped device. This study describes a novel approach to prepare a synaptic transistor by doping conjugated polymers, which can promote the future development of artificial neuromorphic systems.
Relation: ACS Applied Materials & Interfaces
Data Type: article
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