Using inverse analysis to find optimum nano-scale radiative surface patterns to enhance solar cell performance
Abstract Nano-scale surface patterning can provide highly spectral-directional absorption properties for thin film solar cells. Achieving optimal surface patterning is an inverse optimization problem that requires a numerical procedure capable of scrutinizing the space of geometry parameters to search for an optimal solution. In this paper, 2-D and 3-D thin film amorphous silicon (a-Si) solar cells with periodic structures are considered, whereby the surface of the solar cell is textured with rectangular metallic nano-patterns for enhancing the solar absorption spectrum. We use FDTD simulations to solve Maxwell’s equations inside the cell area when subject to standard optical irradiation. By means of several numerical optimization techniques such as the Quasi-Newton BFGS method, random search Simulated Annealing, and Tabu Search, we determine optimal specifications for surface nano-patterns. Invoking constrained optimization tools, we incorporate various types of practical constraints into the optimization programs. The resulting cell structures found by the inverse solvers illustrate enhancement factors as high as 1.52 in solar absorption when silver nano-patterns are used, compared to bare a-Si cells. Results demonstrate the efficiency of the selected optimization techniques and their computational efficiency in contrast with the naive brute force alternative and provide a benchmark for comparing the performances of various local and global optimization methods. The global random search optimization techniques (Simulated Annealing and Tabu Search) tend to perform better than the local method (Quasi-Newton), especially for higher problem dimensions.