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Fabrication of Injectable Micro-Scale Opto- Electronically Transduced Electrodes (MOTEs) for Physiological Monitoring

Sunwoo Lee, Alejandro J. Cortese, Aaron T. Mok, Chunyan Wu, Tianyu Wang, Ju Uhn Park, Conrad Smart, Shahaboddin Ghajari, Devesh Khilwani, Sanaz Sadeghi, Yanxin Ji, Jesse H. Goldberg, Chris Xu, Paul L. McEuen, Alyosha Molnar

2020Journal of Microelectromechanical Systems31 citationsDOIOpen Access PDF

Abstract

In vivo, chronic neural recording is critical to understand the nervous system, while a tetherless, miniaturized recording unit can render such recording minimally invasive. We present a tetherless, injectable micro-scale opto-electronically transduced electrode (MOTE) that is ~60μm × 30μm × 330μm, the smallest neural recording unit to date. The MOTE consists of an AlGaAs micro-scale light emitting diode (μLED) heterogeneously integrated on top of conventional 180nm complementary metal-oxide-semiconductor (CMOS) circuit. The MOTE combines the merits of optics (AlGaAs μLED for power and data uplink), and of electronics (CMOS for signal amplification and encoding). The optical powering and communication enable the extreme scaling while the electrical circuits provide a high temporal resolution (c100μs). This paper elaborates on the heterogeneous integration in MOTEs, a topic that has been touted without much demonstration on feasibility or scalability. Based on photolithography, we demonstrate how to build heterogenous systems that are scalable as well as biologically stable - the MOTEs can function in saline water for more than six months, and in a mouse brain for two months (and counting). We also present handling/insertion techniques for users (i.e. biologists) to deploy MOTEs with little or no extra training. [2020-0080].

Topics & Concepts

ScalabilityCMOSPhotolithographyComputer scienceComputer hardwareElectronicsTransceiverMaterials scienceOptoelectronicsElectrical engineeringElectronic engineeringEngineeringDatabaseNeuroscience and Neural EngineeringPhotoreceptor and optogenetics researchEEG and Brain-Computer Interfaces
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