Jonginn Yun

Genetic-algorithm-based micromagnet morphology optimization for device-relevant magnetic-field-gradient targets.

This project develops a micromagnet morphology-optimization workflow for semiconductor spin-qubit devices. The goal is to search for on-chip magnetic-field-gradient profiles that improve electric-dipole spin-resonance control while avoiding unwanted gradients that can degrade coherence.

I built a scalable evaluation pipeline that integrates micromagnetic simulation with automated optimization loops for rapid design iteration. The workflow uses both mesh-based and polygon-based shape representations, combines genetic-algorithm search with local refinement, and evaluates candidate devices against field-gradient targets relevant to spin-qubit operation.

The central idea is to treat the micromagnet shape itself as an experimental design variable. Instead of assuming a fixed magnet geometry and tuning only device-operation parameters, the optimization searches over physical layouts that can produce a more useful field landscape for spin-qubit control.