Coupled process and device modeling of Cu(In,Ga)Se2 solar cells | IEEE Conference Publication | IEEE Xplore

Coupled process and device modeling of Cu(In,Ga)Se2 solar cells


Abstract:

Point defects directly impact solar cell device performance by limiting the carrier lifetime. In this work, density functional theory calculations are first conducted to ...Show More

Abstract:

Point defects directly impact solar cell device performance by limiting the carrier lifetime. In this work, density functional theory calculations are first conducted to determine the formation energy and diffusion energy barriers of dominant defects in Cu(In,Ga)Se2. Next, continuum models are developed to model the redistribution of defects during cooling and annealing processes. The calculated defect profiles as well as the corresponding capture cross sections and trap energy levels are implemented into device simulation via Shockley-Read-Hall (SRH) recombination models to calculate carrier lifetime and device performance. In that way, a predictive TCAD model is built to optimize the composition and performance of Cu(In,Ga)Se2 solar cells.
Date of Conference: 20-25 June 2021
Date Added to IEEE Xplore: 26 August 2021
ISBN Information:
Print on Demand(PoD) ISSN: 0160-8371
Conference Location: Fort Lauderdale, FL, USA

Funding Agency:


I. Introduction

Photovoltaic devices are indispensable for renewable clean energy systems. Today, silicon-based solar modules dominate the market, but various emerging techniques based on thin-film inorganic semiconductors are rapidly developing. Among thin-film technologies, chalcopyrite Cu(In,Ga)Se2 (CIGS) show excellent light conversion efficiency [1]. Defects in CIGS such as Group III antisites (InCu, GaCu) and copper antisites (CuIII) often possess deep energy levels within the energy bandgap which act as Shockley-Read-Hall (SRH) recombination centers limiting solar cell performance. The defect distribution is highly dependent on composition [2]. Therefore, it is crucial to develop predictive models to explore how composition variations in CIGS absorber layers as well as growth and annealing conditions impact defect profiles and thus affect device performance. However, current TCAD tools do not have models coupling composition-dependent defect distributions with device simulation.

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References

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