PhD Project by Pratik Kusumanchi

Project Title: Miniaturized Solar Cells for Retinal Prosthesis
Group: Biomaterial Microsystems
Supervisor: Stephan Keller

Project Description
In subretinal or epiretinal prosthesis, the artificial restoration of the sight is achieved by electrically stimulating the remaining retinal neurons in a human eye. Principally, the degenerated photoreceptors cause for vision loss in majority of retinal diseases. Thus, state of the art photovoltaic implants was realized for wireless stimulation the ganglion cells (RGCs) in the retina via inner nuclear layer (INL)(subretinal) or by direct RGCs stimulation (epiretinal).

The design consists of a photosensitive area and an electrode with a local/global return, stimulation of the neurons will be carried out by optically illuminating the solar cell with a specific wavelength and using the electrical output to trigger the RGCs. Few parameters (pixel dimensions, electrode dimensions and materials) are to be considered to obtain a visual acuity of 20/200[1]. The state of the art 3-dimensional (3D) active pillar electrodes has gained attention in the past decade due to the reduced proximity of the target. 

Therefore, this PhD project focuses on designing, fabricating, and optimizing a wireless retinal prosthesis (subretinal) specifically involving 3D pillars as active electrodes. Comprehensive research is carried out on the parameters of the prosthesis to design the solar cell array and integrating the pillars in state-of-the-art fabrication procedure. Potential advantages of the fabricated prosthesis are investigated by characterizing the results from ex-vivo and in-vivo electrical, optical, and electrophysiological experiments. 

This project is based on cooperation between DTU Nanolab, Aarhus University Hospital and Aarhus University and is funded by Novo Nordisk Foundation, Exploratory Synergy Program, nr. NNF20O0064628. [1] E. Ho et al., “Characteristics of prosthetic vision in rats with subretinal flat and pillar electrode arrays,” Journal of Neural Engineering, vol. 16, no. 6, Oct. 2019, doi: 10.1088/1741-2552/ab34b3.