We present Montecarlo and Genetic Algorithm optimisations applied to the design of photodetectors based on a transimpedance amplifier and a photodiode. The circuit performance is evaluated with a merit function and the systematic search method is used as a reference. The design parameters are the feedback network components and the photodiode bias voltage. To evaluate the optimisations, we define the relative difference between its merit and the optimum merit obtained by the systematic search. In both algorithms, the relative difference decreases with the number of evaluations, following a power law. The power-law exponent for the Genetic Algorithm is larger than that of Montecarlo (0.74 vs. 0.50). We conclude that both algorithms are advantageous compared to the systematic search method, and that the Genetic Algorithm shows a better performance than Montecarlo.

翻译：我们提出了基于跨阻放大器和光电二极管的光电探测器设计中，蒙特卡洛算法与遗传算法的优化方法。电路性能通过评价函数进行评估，并以系统搜索法作为基准。设计参数包括反馈网络组件和光电二极管偏置电压。为评估优化效果，我们定义了评价函数值与系统搜索所得最优值之间的相对差异。两种算法中，相对差异随评估次数增加呈幂律递减。遗传算法的幂律指数大于蒙特卡洛算法（0.74 对 0.50）。我们得出结论：两种算法均优于系统搜索法，且遗传算法的性能表现优于蒙特卡洛算法。