Coupled Optoelectronic Simulation and Optimization of Thin-Film Photovoltaic Solar Cells

TL;DR

This work presents a coupled optoelectronic design tool for thin-film photovoltaic solar cells with periodic structures. The photonic step uses rigorous coupled-wave analysis (RCWA) to obtain the spatial distribution of absorbed light and electron-hole generation, which is averaged along periodic directions for the electronic step. The electronic step solves a nonlinear 1D drift-diffusion system with local quasi-thermal equilibrium and various recombination mechanisms via a high-order hybridizable discontinuous Galerkin (HDG) method, enabling efficient Newton-based solution and accurate handling of heterojunctions. An optimization loop based on the differential evolution algorithm (DEA) maximizes solar-cell efficiency by adjusting semiconductor layer dimensions and bandgaps, demonstrating a practical pathway to high-performance thin-film devices. The results highlight RCWA convergence behavior, the effectiveness of HDG for nonlinear transport with spatially varying generation, and the potential gains from optimization in devices with periodic metal gratings.

Abstract

A design tool was formulated for optimizing the efficiency of inorganic, thin-film, photovoltaic solar cells. The solar cell can have multiple semiconductor layers in addition to antireflection coatings, passivation layers, and buffer layers. The solar cell is backed by a metallic grating which is periodic along a fixed direction. The rigorous coupled-wave approach is used to calculate the electron-hole-pair generation rate. The hybridizable discontinuous Galerkin method is used to solve the drift-diffusion equations that govern charge-carrier transport in the semiconductor layers. The chief output is the solar-cell efficiency which is maximized using the differential evolution algorithm to determine the optimal dimensions and bandgaps of the semiconductor layers.

Figures (7)

• Figure 1: (a) An academic model of a metal-backed p-i-n solar cell used as an example throughout this paper. The dimensions shown are simply for illustration. (b) A triangular metal grating in air used to test RCWA (of course, this is not a solar cell). Figures are not to scale.
• Figure 2: Global relative errors (a) $e_{E_s}$ and (b) $e_{E_p}$ vs. $N_{\rm t}$ for the simple problem depicted in Fig. \ref{['Ben1']}(b). Choice 1: $\breve{{M}} = \breve{{B}}^{-1}$ and $\breve{{N}} = \breve{{E}}^{-1}$; Choice 2: $\breve{{M}}= \breve{{B}}^{-1}$ and $\breve{{N}} = \breve{{B}}$; Choice 3: $\breve{{M}} = \breve{{E}}$ and $\breve{{N}} = \breve{{E}}^{-1}$; Choice 4: $\breve{{M}} = \breve{{E}}$ and $\breve{{N}}= \breve{{B}}$. A straight line shows the empirical order of convergence.
• Figure 3: Relative errors (a) $e_{E_s}$ and (b) $e_{E_p}$ vs. $N_{\rm t}$ in the semiconductor region of the solar cell depicted in Fig. \ref{['Ben1']}(a). Choice 1: $\breve{{M}} = \breve{{B}}^{-1}$ and $\breve{{N}} = \breve{{E}}^{-1}$; Choice 2: $\breve{{M}} = \breve{{B}}^{-1}$ and $\breve{{N}} = \breve{{B}}$; Choice 3: $\breve{{M}} = \breve{{E}}$ and $\breve{{N}}= \breve{{E}}^{-1}$; Choice 4: $\breve{{M}} = \breve{{E}}$ and $\breve{{N}}= \breve{{B}}$. A straight line shows the empirical order of convergence.
• Figure 4: Relative errors in $P_{\rm max}$, $J_{\rm SC}$, $V_{\rm OC}$, and $FF$ against $P_{\rm deg}\in[2,8]$, when $d_{\rm z}= 2$ nm and $I_{\rm deg} = 10$.
• Figure 5: Relative errors in $P_{\rm max}$, $J_{\rm SC}$, $V_{\rm OC}$, and $FF$ against $d_{\rm z}\in[1,20]$ nm, when $P_{\rm deg}= 5$ and $I_{\rm deg}=10$.