Textures for High‐Efficiency Solar Cells

Vidya Ganapati, Swarthmore College, Swarthmore, PA, USA

Abstract : Improvements to the efficiency of photovoltaics lowers the cost of solar electricity, as higher efficiency causes overhead costs to decrease. In this talk, I will discuss how optical design considerations, such as surface texturing and back mirror design, are crucial to achieving efficiencies that come close to thermodynamic limits.

In a at open circuit voltage, ideally all absorbed photons are re‐emitted out the front surface. A back reflector is important to obtaining high efficiency, as it suppresses photon emission out the back surface. I will show how the back mirror concept can be extended to the sub‐cells of a multijunction cell, with the use of air gaps as “intermediate” reflectors. The implementation of intermediate reflectors in a 4‐junction cell has led to a record‐breaking efficiency of 38.8%.

Surface texturing of solar cells also increases efficiency by both increasing photon absorption and photon emission out the front surface. It is known that in geometrical optics, a maximum 4n2 absorption enhancement factor can be achieved (the Yablonovitch limit) by randomly texturing the surface of the solar cell, where n is the material refractive index. This ray‐optics absorption enhancement limit only holds when the thickness of the solar cell is much greater than the optical wavelength. In subwavelength thin films, the fundamental questions remain unanswered: 1) what is the subwavelength absorption enhancement limit and 2) what surface texture realizes this optimal absorption enhancement? We turn to computational electromagnetic optimization in order to design nanoscale photonic crystal textures for light trapping in subwavelength thin films. For high‐index thin films, in the weakly absorbing limit, our optimized surface textures yield an angle‐ and frequency‐averaged enhancement factor ~39, performing roughly 30% better than randomly textured structures..

Biographical Sketch: Vidya Ganapati is an Assistant Professor of Engineering at Swarthmore College. She was previously a Postdoctoral Associate at Verily Life Sciences (formerly Google[x]) developing deep learning methods for decision‐making in robotic laparoscopic surgery. She received her Ph.D. and M.S. in Electrical Engineering & Computer Science at the University of California, Berkeley, advised by Professor Eli Yablonovitch. Her research in high‐ efficiency photovoltaics helped enable the development of a solar cell with record efficiency. She has been a recipient of the CITRIS Athena Early Career Award, the Department of Energy Office of Science Graduate Fellowship, and the UC Berkeley Chancellor's Fellowship. She received an S.B. from the Massachusetts Institute of Technology in Electrical Engineering. Her current research interests include using optimization, machine learning, and simulation for optical system design, with applications in photovoltaics and bioimaging.

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