Hello, I'm Riccardo

I develop a fully custom software ecosystem for electronic design, written in Rust for performance and Python for analysis and automation. These tools allow me to simulate, optimize, and visualize thousands of circuit configurations with high precision and reproducibility.

I use genetic algorithms to automatically evolve circuit designs and component selections toward optimal efficiency, thermal performance, and cost. Each generation tests and evaluates thousands of candidates through simulation, applying natural selection principles to converge on the best trade-offs between competing objectives — such as size, losses, EMI, and cost. The result is a data-driven Pareto front of high-performance solutions that reveal what’s truly achievable within given design constraints.

I integrate detailed thermal models into the optimization loop so that each candidate design is evaluated not only for electrical performance but also for its temperature rise and cooling efficiency. This ensures that selected components operate safely under realistic thermal loads, reducing derating and improving lifetime reliability — achieving performance without exceeding thermal limits.

Using Monte Carlo analysis, I test how each optimized design behaves under variations in component tolerances, supply voltage, and temperature. This statistical approach identifies sensitive parameters and validates that the final design maintains compliance and performance across real-world manufacturing and environmental conditions.