Authors: Z-L Lu
Affilation: National Chiao Tung University, Taiwan
Pages: 583 - 586
Keywords: silicon thin-film solar cell, transport model, numerical simulation, genetic algorithm, simulation-based, evolutionary methodology, efficiency, p-i-n structure
Solar cell , which can provides renewable and clean energy by converting sunlight to electrical power, is one of the most promising energy technologies in order to relieve the impact of the climate change. Yet, before in replace of fossil fuel for electrical power generation, the fabrication cost of solar cells is urgently to be reduced and the energy conversion efficiency increased significantly At present, crystalline silicon (c-Si), polycrystalline silicon (poly-Si), and amorphous silicon (a-Si) are the main developed siliconbased materials exploited in solar energy industries. Optimal design of thin-film solar cell in pursuit of high energy conversion efficiency can be reached in a trial-and-error engineering way. On the other hand, genetic algorithm (GA) is a population-based global searching optimization method based on the mechanism of natural selection, and often considered as the most famous branch in evolutionary algorithms. In theory, GA with appropriate elitist policy can guarantee the acquirement of the best solution in the global domain and generally can provide many near-optimal selections of the problem. In practice, GA can provide promising observation and are popularly applied in engineering domains with modification in various types . For a basic p-i-n structure of a-Si thin-film solar cell, the efficiency is 5% . Thus, characteristic optimization of a-Si solar cell using GA is an interesting approach for solar cell technologies. In this work, a device simulation-based GA is applied to optimize electrical properties of a-Si thin-film solar cells under illumination. With numerically solving a set of transport equations in device simulation, the optimal design parameters of explored solar cell can be obtained via GA method. The iteration of evolution is terminated once the convergent solution is acquired. The evolutionary technique enables us to optimize the key electrical characteristics, such as short-circuited current (Jsc), open-circuited voltage (Voc), fill factor (FF), and energy conversion efficiency (η) of the explored p-i-n a-Si solar cell.