人们对消耗大量能源的服务越来越感兴趣,例如大型语言模型(LLM),全世界正在对神经形态硬件的突触设备进行研究。然而,各种复杂的过程对于突触特性的实现是有问题的。这里,突触特征是通过一种新的方法来实现的,即共轭聚合物的侧链控制。所开发的设备展示了生物大脑的特征,尤其是尖峰时间依赖性可塑性(STDP),高通滤波,和长期增强/抑制(LTP/D)。此外,制造的突触设备显示出增强的非易失性特征,例如长保留时间(≈102s),Gmax/Gmin的高比率,高线性度,和可靠的循环耐久性(≈103脉冲)。这项研究提出了一个新的途径,为下一代神经形态计算通过调节共轭聚合物与侧链控制,从而实现高性能的突触特性。
Interest has grown in services that consume a significant amount of energy, such as large language models (LLMs), and research is being conducted worldwide on synaptic devices for neuromorphic hardware. However, various complex processes are problematic for the implementation of synaptic properties. Here, synaptic characteristics are implemented through a novel method, namely side chain control of conjugated polymers. The developed devices exhibit the characteristics of the biological brain, especially spike-timing-dependent plasticity (STDP), high-pass filtering, and long-term potentiation/depression (LTP/D). Moreover, the fabricated synaptic devices show enhanced nonvolatile characteristics, such as long retention time (≈102 s), high ratio of Gmax/Gmin, high linearity, and reliable cyclic endurance (≈103 pulses). This study presents a new pathway for next-generation neuromorphic computing by modulating conjugated polymers with side chain control, thereby achieving high-performance synaptic properties.