Optimisation of photodetectors design: comparison between Montecarlo and Genetic Algorithms

TL;DR

This paper compares Montecarlo and Genetic Algorithm optimization methods for photodetector design, demonstrating that both outperform systematic search, with Genetic Algorithms showing superior efficiency and convergence.

Contribution

The study provides a comparative analysis of Montecarlo and Genetic Algorithms for optimizing photodetector circuits, highlighting their convergence behavior and efficiency advantages.

Findings

Genetic Algorithm has a higher power-law exponent (0.74) than Montecarlo (0.50).

Both algorithms outperform systematic search in optimization efficiency.

Genetic Algorithm shows better performance than Montecarlo in photodetector design optimization.

Abstract

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.