Optimisation of photodetectors design: comparison between Montecarlo and Genetic Algorithms
CoRR(2024)
摘要
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.
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