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Updated: Jan 9, 2026

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Published on: May 11, 2014

Artificial-intelligence-based design optimization of low-noise transimpedance amplifiers for optical detection.

Patricia M E Vázquez¹, Ligia Ciocci Brazzano^1,2, Francisco E Veiras¹

¹Universidad de Buenos Aires, Facultad de Ingeniería, Departamento de Física, GLOmAe, Ciudad Autónoma de Buenos Aires, Argentina.

The Review of Scientific Instruments

|December 8, 2025

Summary

Artificial intelligence (AI) optimizes transimpedance amplifiers for optical detection using a Genetic Algorithm (GA). This AI-driven design significantly outperforms traditional Monte Carlo methods in efficiency and accuracy for photodetector development.

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Area of Science:

Electrical Engineering
Optical Instrumentation
Artificial Intelligence

Background:

Transimpedance amplifiers are crucial for electro-optical systems, particularly in optical ultrasound detection.
Optimizing these amplifiers is key to improving system performance and reducing noise.

Purpose of the Study:

To present an AI-based design optimization of transimpedance amplifiers for optical detection using a Genetic Algorithm (GA).
To explore the influence of GA parameters on optimization performance.
To compare AI-based optimization with Monte Carlo (MC) methods and experimental validation.

Main Methods:

Utilized a Genetic Algorithm (GA) for the design optimization of photodetectors.
Investigated the impact of GA parameters such as population size, generations, and mutation rate.
Compared GA optimization results against Monte Carlo (MC) optimization and systematic search benchmarks.
Experimentally validated the AI-based optimized photodetector.

Main Results:

AI-based optimization with specific GA parameters (population size 1000, 10 generations, 5% mutation) required only 104 evaluations, achieving results within 0.12% of the maximum merit.
MC optimization needed significantly more evaluations (3.4 × 10^5) for comparable statistical results.
Demonstrated power-law scaling of performance with initial population size for both GA (exponent 1.2) and MC (exponent 0.88).
Experimental validation confirmed the AI-based optimization's accuracy.

Conclusions:

AI-based design optimization using GA is a highly efficient and promising method for developing low-noise transimpedance amplifiers.
This approach offers superior performance and reduced computational cost compared to traditional methods like MC optimization.
The validated AI method is applicable to various photodetector designs, including those for ultrasound detection, general-purpose use, and quantum processing research.