Department of Mechanical Engineering, The University of Hong Kong, Pok Fu Lam, Hong Kong.
Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA.
Nat Commun. 2021 Apr 30;12(1):2480. doi: 10.1038/s41467-021-22680-5.
Associative learning, a critical learning principle to improve an individual's adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning circuits. Here, by adopting a poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran composite as the active channel, we present a non-volatile organic electrochemical transistor that shows a write bias less than 0.8 V and retention time longer than 200 min without decoupling the write and read operations. By incorporating a pressure sensor and a photoresistor, a neuromorphic circuit is demonstrated with the ability to associate two physical inputs (light and pressure) instead of normally demonstrated electrical inputs in other associative learning circuits. To unravel the non-volatility of this material, ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy and grazing-incidence wide-angle X-ray scattering are used to characterize the oxidation level variation, compositional change, and the structural modulation of the poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran films in various conductance states. The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications.
关联学习是提高个体适应性的关键学习原则,虽然已有少数有机电化学器件对其进行了模拟,但复杂的偏置方案、高写入电压以及过程不可逆性阻碍了关联学习电路的进一步发展。在这里,我们采用聚(3,4-亚乙基二氧噻吩):对甲苯磺酸盐/聚四氢呋喃复合物作为有源通道,展示了一种非易失性有机电化学晶体管,其写入偏置小于 0.8V,保留时间超过 200min,而无需解耦写入和读取操作。通过结合压力传感器和光电阻器,我们演示了一种具有关联两个物理输入(光和压力)的神经形态电路的能力,而不是通常在其他关联学习电路中演示的电输入。为了揭示这种材料的非易失性,我们使用紫外可见近红外光谱、X 射线光电子能谱和掠入射广角 X 射线散射来表征聚(3,4-亚乙基二氧噻吩):对甲苯磺酸盐/聚四氢呋喃薄膜在不同电导状态下的氧化水平变化、组成变化和结构调制。关联学习电路的实现以及对非易失性材料的理解代表了有机电化学器件在神经形态应用中的重要进展。