J Comput Electron (2014) 13:323–328 DOI 10.1007/s10825-013-0533-0 Geometrical and physical optimization of a photovoltaic cell by means of a genetic algorithm Giuseppe Alì · Francesco Butera · Nella Rotundo Published online: 19 November 2013 © Springer Science+Business Media New York 2013 Abstract A methodology for the geometrical and physical optimization of a photovoltaic cell is proposed, which makes use of a detailed distributed model for the device simula- tion and a genetic algorithm. For the numerical simulation of the device, a TCAD simulator is used, appropriately in- terfaced with the genetic algorithm. Since the parameters to be optimized are geometrical, each simulation requires a different mesh grid, which is automatically set within the genetic algorithm optimization cycle. The evaluation of the fitness function requires the post-processing of the output of the device simulation, which is performed by another ex- ternal software, also interfaced with the genetic algorithm. The feasibility of this methodology is assessed on a homo- geneous emitter solar cell, with some relevant free param- eters, related to the number of fingers in a cell and to the doping profile of the emitter. The parameters which maxi- mize the efficiency of the cell are determined by using the proposed procedure. Keywords Photovoltaics · Silicon · Solar cell · Genetic algorithm · Optimization · Continuous and integer variables G. Alì (B ) · F. Butera Department of Physics, University of Calabria, Arcavacata di Rende, 87036 Cosenza, Italy e-mail: giuseppe.ali@unical.it G. Alì INFN, Gruppo Collegato di Cosenza, Arcavacata di Rende, 87036 Cosenza, Italy N. Rotundo Department of Mathematics and Statistics, McGill University, Canada, Italy 1 Introduction The last report on photovoltaic industry in Europe, published in October 2012, foresees that, within 2050, as much as the 21 % of the power supply worldwide, that is, 6750 TWh, will be generated by photovoltaic cells [1]. Since the first so- lar cell was realized 55 years ago [2], the cost has decreased of a factor 200, and this value is foreseen to decrease even further [3]. These prevision refer to a well standardized tech- nology, with a well established structure, and any advantage can be achieved only via a careful optimization of the exist- ing cells in order to maximize the efficiency. The optimization of specific device parameters requires the use of a TCAD simulator and repeated simulations. Moreover, if the parameters have a geometrical nature, that is, if the numerical grid used for each simulation is altered by changing them, then each simulation requires a preliminary grid generation. Now, performing this sequence of grid gen- erations and simulations can be computationally very chal- lenging. Even more so, if one wishes to automize the process and link the simulations to an optimization algorithm. A possible alternative would be to reduce the complexity of the device model, by considering 1D versions of it [4], or by constructing some equivalent circuit formulations [5]. We also mention two more recent papers [6, 7] which use this approach to innovative PV cells. Then, a classical opti- mization algorithm can be successfully used, but eventually one has to check the validity of the results thus obtained, resorting to a detailed model and a TCAD simulator. In this paper we present an optimization strategy, mak- ing use of a detailed distributed model for the PV cell, and based on a genetic algorithm (GA) [8]. The idea of GA is inspired by population dynamics in biology. The parameters to be optimized are collected in an array, which is viewed as the chromosome of an individual. Then a population of indi- viduals evolves, according to biology-inspired rules, so that