Novel Carrier Selective Contacts of Silicon Based Solar Cells

  • Jingxuan Kang

Student thesis: Doctoral Thesis

Abstract

Renewable and clean energy is urgently needed to cope with the climate crisis. Photovoltaics (PV) has been the fastest growing technology in the clean energy market due to its low cost, and the abundance of solar energy. The capacity of silicon-based PV is rapidly expanding with evolving technologies. Passivating the solar cell’s electrical contacts is a widely accepted strategy for the PV industry to improve device power conversion efficiency (PCE). Polycrystalline silicon (Poly-Si) passivating contacts are one of the promising concepts in the emerging class of passivating contacts. In this dissertation, the passivation mechanism of Poly-Si passivating contacts is investigated. Moreover, the influence of dopant diffusion on the passivation quality is revealed. To address the side-effects of dopant diffusion, a thin buffer layer is inserted between the Poly-Si(p) layer and the $SiO_x$ layer. With such a buffer layer, the passivation of the Poly-Si passivating contact is improved, which in turn, enhances the device PCE. In addition to passivating contacts, this dissertation also explores carrier-selective contact of crystalline silicon (c-Si) and low work function metal – Li. Li is a very reactive metal which makes the fabrication process a challenge. To overcome such a challenge, the c-Si/ Li contact is fabricated by thermally decomposing stable $Li_3N$ powder instead of metal evaporation. The c-Si/Li contact shows an excellent electron-selective transport performance with a 0.39 eV energy barrier. Full-area Si/Li rear contact devices are fabricated, and >19% PCE and >80% fill factor are achieved. To accelerate the device optimization, a physical model embedded machine-learning approach is applied to transparent conductive oxide (TCO) materials optimization. In this work, empirical correlations between sputtering parameters and the deposited TCOs’ electrical properties are established. Then a Bayesian Parameter Estimation (BPE) algorithm is applied to learn the empirical model. With this BPE network, the TCOs’ electrical properties are successfully predicted with limited material characterizations. Thanks to the combination of BPE and a physical model network, the material optimization process is significantly accelerated. In summary, this dissertation explores different aspects to develop novel passivating and carrier-selective contacts for c-Si solar cells, and introduces an approach to accelerate the development processes.
Date of AwardSep 2022
Original languageEnglish (US)
Awarding Institution
  • Physical Science and Engineering
SupervisorStefaan De Wolf (Supervisor)

Keywords

  • Silicon solar cell
  • Poly-Si passivating contact
  • carrier-selective contact
  • machine learning
  • lithium silicon contact

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