journal article·2020-05Open access

Infrared Absorption Enhancement Using Periodic Inverse Nanopyramids in Crystalline-Silicon Bottom Cells for Application in Tandem Devices

Arsalan Razzaq, Valerie Depauw, Hariharsudan Sivaramakrishnan Radhakrishnan, Jinyoun Cho, Ivan Gordon, Jozef Szlufcik, Yaser Abdulraheem, Jef Poortmans

IEEE Journal of Photovoltaics· Volume 10· Issue 3

Abstract

Carefully tailored periodic nanostructures on the light wavelength scale, such as diffraction gratings, benefit from wave optics for efficiently trapping the weakly absorbing infrared photons in crystalline-silicon (c-Si) absorbers. In contrast with the conventional random pyramid texture, diffraction gratings can be designed to target specific wavelength ranges by the selection of the grating pitch. Absorption enhancement at infrared wavelengths in a silicon solar cell is especially desired when it operates below a perovskite top cell in a tandem device. In this article, inverse nanopyramid gratings of 800 nm pitch are proposed as an alternative front-surface texture to random pyramids in silicon heterojunction devices with interdigitated back contacts that are to be used as bottom cells in four-terminal perovskite/c-Si tandem devices. By doing so, we report a short-circuit current density gain of 0.53 mA/cm 2 with respect to the random pyramid texturing for the bottom c-Si cell. The rationale to substitute random pyramids by inverse nanopyramid gratings is, however, not justified in single-junction operation despite achieving the power conversion efficiency of 22.3% since the degraded optical performance at shorter wavelengths offsets the absorption enhancement at longer wavelengths, resulting in similar levels of short-circuit current densities for both texture types.
Source: openalex_inverted_index · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Thin-Film Transistor Technologies · Silicon and Solar Cell Technologies · Silicon Nanostructures and Photoluminescence

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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