In-Materio Reservoir Computing in a Sulfonated Polyaniline Network
In-Materio Reservoir Computing in a Sulfonated Polyaniline Network
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DOI：
10.1002/adma.202102688
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发表时间：
2021-09-17
影响因子：
29.4
通讯作者：
Matsumoto, Takuya
中科院分区：
文献类型：
--
作者：
Usami, Yuki;van de Ven, Bram;Matsumoto, Takuya
采用简单滴铸法制备了磺化聚苯胺（SPAN）有机电化学网络器件（OEND），用于储层计算（RC）。SPAN网络具有与湿度相关的电特性。在高湿条件下，SPAN OEND主要表现为离子传导，包括双电层的充电和离子扩散。随着湿度的增加，电流-电压特性的非线性和滞后性逐渐增大。丰富的动态输出行为表明每个电极的变化很大，这改善了由于无序网络的RC性能。对于RC，波形产生和短期记忆任务是通过输出的线性组合来实现的。波形任务准确率达到90%，短期记忆任务计算的记忆容量达到33.9%。改进的辐条数字分类仅通过12个输出就实现了60%的准确率，表明SPAN OEND由于其丰富的动态和非线性电子特性的结合，可以管理RC中的时间序列动态数据操作。结果表明，基于span的电化学体系可以利用其固有的物理化学行为应用于基于材料的计算。
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.