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基于 CMOS 的神经传感器件的无源和有源微纳电极的多尺度模拟分析。

Multiscale simulation analysis of passive and active micro/nanoelectrodes for CMOS-based neural sensing devices.

机构信息

Dipartimento di ingegneria Enzo Ferrari, University of Modena and Reggio Emilia, Modena, Italy.

Polytechnical Department of Engineering and Architecture, University of Udine, Udine, Italy.

出版信息

Philos Trans A Math Phys Eng Sci. 2022 Jul 25;380(2228):20210013. doi: 10.1098/rsta.2021.0013. Epub 2022 Jun 6.

Abstract

Neuron and neural network studies are remarkably fostered by novel stimulation and recording systems, which often make use of biochips fabricated with advanced electronic technologies and, notably, micro- and nanoscale complementary metal-oxide semiconductor (CMOS). Models of the transduction mechanisms involved in the sensor and recording of the neuron activity are useful to optimize the sensing device architecture and its coupling to the readout circuits, as well as to interpret the measured data. Starting with an overview of recently published integrated active and passive micro/nanoelectrode sensing devices for studies fabricated with modern (CMOS-based) micro-nano technology, this paper presents a mixed-mode device-circuit numerical-analytical multiscale and multiphysics simulation methodology to describe the neuron-sensor coupling, suitable to derive useful design guidelines. A few representative structures and coupling conditions are analysed in more detail in terms of the most relevant electrical figures of merit including signal-to-noise ratio. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

摘要

神经元和神经网络的研究得益于新型的刺激和记录系统,这些系统通常利用先进电子技术制造的生物芯片,特别是微纳尺度互补金属氧化物半导体(CMOS)。用于传感器和神经元活动记录的转换机制模型有助于优化传感设备的架构及其与读出电路的耦合,也有助于解释测量数据。本文从最近发表的基于现代(基于 CMOS 的)微纳技术制造的用于研究的集成有源和无源微/纳电极传感设备的概述开始,提出了一种混合模式器件-电路数值分析多尺度多物理模拟方法来描述神经元-传感器的耦合,有助于得出有用的设计准则。根据包括信噪比在内的最重要的电性能指标,本文详细分析了一些具有代表性的结构和耦合条件。本文是“高级神经技术:为健康和福祉转化创新”主题特刊的一部分。

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