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Bilayer-Nanomesh Transparent Neuroelectrodes on 10μm-Thick PDMS.

Jaehyeon Ryu^1,2, Yi Qiang², Dongyeol Jang²

¹Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA.

Technical Digest. International Electron Devices Meeting

|February 17, 2023

Summary

Researchers developed a thin, transparent microelectrode array for neural interfaces. This device enables simultaneous electrical recording and optical measurements with minimal brain tissue mismatch, improving neural interfacing technology.

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Area of Science:

Neuroscience
Materials Science
Biomedical Engineering

Background:

Transparent electrode arrays are crucial for neural interfacing, allowing simultaneous electrophysiological recording and optical measurements.
Soft and thin devices offer advantages due to reduced mechanical mismatch with delicate brain tissue.

Purpose of the Study:

To demonstrate a novel bilayer-nanomesh-based transparent microelectrode array (MEA) on an ultrathin Polydimethylsiloxane (PDMS) substrate.
To evaluate the performance, reliability, and optical transparency of the fabricated MEA.

Main Methods:

Fabrication of a 32-channel bilayer-nanomesh microelectrode array on a 10μm thin PDMS substrate.
Characterization of electrode performance, device reliability, optical transparency, surface properties, and sterilization compatibility.

Main Results:

Successfully fabricated a 32-channel transparent MEA with a total device thickness of only 10μm.
Achieved excellent electrode performance, high device reliability, and superior optical transparency.
Demonstrated successful hydrophilic surface modification and compatibility with sterilization processes.

Conclusions:

The developed bilayer-nanomesh transparent MEA on ultrathin PDMS is a promising platform for advanced neural interfacing.
The device's properties are suitable for minimizing mechanical stress on neural tissue and enabling multimodal recordings.
The demonstrated fabrication process and material properties support potential clinical translation for neural interfaces.