2025 · Science Advances
2018
Soft Ultrathin Silicon Electronics for Soft Neural Interfaces
A. Thukral , F. Ershad , N. Enan , Z. Rao , C. Yu
Abstract
Techniques for capturing electrical signals from the brain corresponding to most of the activities (actual or imagined) generated by neuron firing have provided neurologists with a fascinating path to decoding and studying this complex system. Technology to increase the spatiotemporal resolution of brain mapping tools has been advancing, aiming at highly dense electrode sites with the long-term stability of devices and negligible damage to brain tissue. In this article, we discuss some of the recent advances in implantable soft silicon-based electronics devices that are devised for the long-term, minimally invasive, high-resolution recording of neural signals. We briefly introduce the different neural action-potential signals classified for different motor activities, and our review progresses through the advantages and acute requirements of developing high-resolution neural interface devices to promote progress in brain–machine interface (BMI) research. The requirements of high-density electrode coverage on the brain surface and the difficulty in achieving such density with the general design of neural interface devices that use passive electrodes are discussed. We also examine the recent advances in various neural devices with active electronic architectures, and the efficacy of silicon (Si) nanomembrane processing techniques in the construction of soft neural mapping tools.
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A. Thukral, F. Ershad, N. Enan, Z. Rao, C. Yu (2018). Soft Ultrathin Silicon Electronics for Soft Neural Interfaces. IEEE Nanotechnology Magazine 12(1), 21–34. https://doi.org/10.1109/MNANO.2017.2781290
BibTeX · Thukral2018SoftUltrathin
@article{Thukral2018SoftUltrathin,
title = {{Soft Ultrathin Silicon Electronics for Soft Neural Interfaces}},
author = {A. Thukral and F. Ershad and N. Enan and Z. Rao and C. Yu},
journal = {IEEE Nanotechnology Magazine},
year = {2018},
volume = {12},
number = {1},
pages = {21--34},
doi = {10.1109/MNANO.2017.2781290},
url = {https://doi.org/10.1109/MNANO.2017.2781290},
} Related publications
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