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arXiv 1806.04748physics.app-phcs.ET
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基于有机电化学晶体管阵列的神经形态时变模式分类

Neuromorphic Time-Dependent Pattern Classification with Organic Electrochemical Transistor Arrays

Sebastien Pecqueur, Maurizio Mastropasqua Talamo, David Guerin, Philippe Blanchard, Jean Roncali, Dominique Vuillaume, Fabien Alibart

摘要:

基于高度可变的神经细胞单元的自下而上的组装,神经系统可以达到与微电子中使用的标准材料和器件相比无与伦比的性能水平。在硬件中再现这些基本概念可能会彻底改变用于信息处理的材料和设备工程。在这里,我们提出了一种创新的方法,依赖于离子电子材料和固有的设备物理显示模式分类的12个单元的生物传感阵列。我们使用水库计算和学习的概念,以400纳米沟道长度的有机电化学晶体管(OECT)的离子动力学的基础上,演示相关的计算。我们表明,这种方法有效地应对高水平的可变性,通过自下而上的制造,使用一种新的电聚合聚合物,使离子电子器件的功能和材料在电解质中的稳定性。我们研究了阵列大小和可变性对实时分类任务性能的影响,为新的嵌入式传感和处理方法铺平了道路。

英文摘要:

Based on bottom-up assembly of highly variable neural cells units, the nervous system can reach unequalled level of performances with respect to standard materials and devices used in microelectronic. Reproducing these basic concepts in hardware could potentially revolutionize materials and device engineering which are used for information processing. Here, we present an innovative approach that relies on both iono-electronic materials and intrinsic device physics to show pattern classification out of a 12-unit bio-sensing array. We use the reservoir computing and learning concept to demonstrate relevant computing based on the ionic dynamics in 400-nm channel-length organic electrochemical transistor (OECT). We show that this approach copes efficiently with the high level of variability obtained by bottom-up fabrication using a new electropolymerizable polymer, which enables iono-electronic device functionality and material stability in the electrolyte. We investigate the effect of the array size and variability on the performances for a real-time classification task paving the way to new embedded sensing and processing approaches.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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