In-Materio Reservoir Computing in a Sulfonated Polyaniline Network,Advanced Materials

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In-Materio Reservoir Computing in a Sulfonated Polyaniline Network
Advanced Materials ( IF 27.4 ) Pub Date : 2021-09-17 , DOI: 10.1002/adma.202102688
Yuki Usami _{1,

2,

3} , Bram van de Ven ₄ , Dilu G Mathew ₄ , Tao Chen ₄ , Takumi Kotooka ₂ , Yuya Kawashima ₁ , Yuichiro Tanaka _{2,

3} , Yoichi Otsuka ₁ , Hiroshi Ohoyama ₁ , Hakaru Tamukoh _{2,

3} , Hirofumi Tanaka _{2,

3} , Wilfred G van der Wiel ₄ , Takuya Matsumoto ₁

Affiliation

A sulfonated polyaniline (SPAN) organic electrochemical network device (OEND) is fabricated using a simple drop-casting method on multiple Au electrodes for use in reservoir computing (RC). The SPAN network has humidity-dependent electrical properties. Under high humidity, the SPAN OEND exhibits mainly ionic conduction, including charging of an electric double layer and ionic diffusion. The nonlinearity and hysteresis of the current–voltage characteristics progressively increase with increasing humidity. The rich dynamic output behavior indicates wide variations for each electrode, which improves the RC performance because of the disordered network. For RC, waveform generation and short-term memory tasks are realized by a linear combination of outputs. The waveform task accuracy and memory capacity calculated from a short-term memory task reach 90% and 33.9, respectively. Improved spoken-digit classification is realized with 60% accuracy by only 12 outputs, demonstrating that the SPAN OEND can manage time series dynamic data operation in RC owing to a combination of rich dynamic and nonlinear electronic properties. The results suggest that SPAN-based electrochemical systems can be applied for material-based computing, by exploiting their intrinsic physicochemical behavior.

中文翻译：

磺化聚苯胺网络中的物质储层计算

磺化聚苯胺 (SPAN) 有机电化学网络装置 (OEND) 是在多个 Au 电极上使用简单的滴铸方法制造的，用于储层计算 (RC)。SPAN 网络具有依赖于湿度的电气特性。在高湿度下，SPAN OEND 主要表现出离子传导，包括双电层充电和离子扩散。电流-电压特性的非线性和滞后性随着湿度的增加而逐渐增加。丰富的动态输出行为表明每个电极的变化很大，由于无序网络，这提高了 RC 性能。对于 RC，波形生成和短期记忆任务是通过输出的线性组合来实现的。短期记忆任务计算的波形任务准确率和记忆容量分别达到 90% 和 33.9。仅通过 12 个输出就实现了改进的语音数字分类，准确率为 60%，这表明 SPAN OEND 可以管理 RC 中的时间序列动态数据操作，因为它结合了丰富的动态和非线性电子特性。结果表明，通过利用其固有的物理化学行为，基于 SPAN 的电化学系统可以应用于基于材料的计算。

更新日期：2021-09-17

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