神经形态工程学
共聚物
仿真
MNIST数据库
材料科学
合理设计
计算机科学
纳米技术
离子键合
侧链
离子液体
理论(学习稳定性)
化学
烷基
生物系统
离子
人工神经网络
组合化学
工作(物理)
非易失性存储器
扁桃体
离子电导率
晶体管
机制(生物学)
链条(单位)
作者
Yoohyeon Jang,Junho Sung,Suhui Sim,Sein Chung,Young Un Jeon,Maozhong An,Minju Kim,Sung Yun Son,Jaewon Lee,Eunho Lee
摘要
Organic electrochemical synaptic transistors (OESTs) are attracting growing attention for neuromorphic computing, yet their long-term stability remains constrained by uncontrolled ion dynamics. Previous studies have incorporated glycol side chains to facilitate ionic transport, but a systematic understanding of how copolymerization with hydrophobic alkyl units governs ion doping and retention is still lacking. Here, we establish a rational backbone-side chain copolymer design strategy that precisely regulates ionic interactions, crystallinity, and charge transport. We also reveal clear correlations between copolymer structure, ion dedoping dynamics, and nonvolatile retention. These structural advantages enable the faithful emulation of key biological behaviors including paired-pulse facilitation, spike-timing dependent plasticity, and long-term potentiation/depression (LTP/D) with high linearity and stability. Based on these properties, the device achieved a high accuracy of 94.1% in ANN-based recognition simulations for MNIST handwritten digits. This work demonstrates that systematic glycol-alkyl copolymer engineering provides a robust and predictive design principle for high-performance neuromorphic synapses, moving beyond empirical side-chain modifications.
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