Optimisation of photodetectors design: comparison between Montecarlo and Genetic Algorithms

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.

Figures (14)

• Figure 1: Photodetector based on a transimpedance amplifier. The feedback network components are shown in red and green. The photodiode bias voltage node is indicated in blue.
• Figure 2: Flow diagram of the general photodetectors design method.
• Figure 3: Upper panel: unilateral merit function; lower panel: bilateral merit function. We choose these functions to weight the performance variables ($x$).
• Figure 4: Flow diagram of the Montecarlo algorithm.
• Figure 5: Flow diagram of the optimisation with the Genetic Algorithm, initialized with $N_{C}$ chromosomes.