Andrew James Phillips
Ph.D. Student in Electrical Engineering, admitted Autumn 2020
All Publications
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A 1024-Channel 268-nW/Pixel 36 x 36 μm<SUP>2</SUP>/Channel Data-Compressive Neural Recording IC for High-Bandwidth Brain-Computer Interfaces
IEEE JOURNAL OF SOLID-STATE CIRCUITS
2023
View details for DOI 10.1109/JSSC.2023.3344798
View details for Web of Science ID 001137386500001
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Direct-print three-dimensional electrodes for large- scale, high-density, and customizable neural inter- faces.
bioRxiv : the preprint server for biology
2023
Abstract
Silicon-based planar microelectronics is a powerful tool for scalably recording and modulating neural activity at high spatiotemporal resolution, but it remains challenging to target neural structures in three dimensions (3D). We present a method for directly fabricating 3D arrays of tissue-penetrating microelectrodes onto silicon microelectronics. Leveraging a high-resolution 3D printing technology based on 2-photon polymerization and scalable microfabrication processes, we fabricated arrays of 6,600 microelectrodes 10-130 μm tall and at 35-μm pitch onto a planar silicon-based microelectrode array. The process enables customizable electrode shape, height and positioning for precise targeting of neuron populations distributed in 3D. As a proof of concept, we addressed the challenge of specifically targeting retinal ganglion cell (RGC) somas when interfacing with the retina. The array was customized for insertion into the retina and recording from somas while avoiding the axon layer. We verified locations of the microelectrodes with confocal microscopy and recorded high-resolution spontaneous RGC activity at cellular resolution. This revealed strong somatic and dendritic components with little axon contribution, unlike recordings with planar microelectrode arrays. The technology could be a versatile solution for interfacing silicon microelectronics with neural structures and modulating neural activity at large scale with single-cell resolution.
View details for DOI 10.1101/2023.05.30.542925
View details for PubMedID 37398164
View details for PubMedCentralID PMC10312573
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Efficient Modeling and Calibration of Multi-Electrode Stimuli for Epiretinal Implants
IEEE. 2023
View details for DOI 10.1109/NER52421.2023.10123907
View details for Web of Science ID 001009053700189