Physics-Consistent Hybrid AI for Quantum Device Manufacturing

Quantum device manufacturing requires atomic-scale precision across complex, multi-physics processes such as atomic layer deposition, plasma etching, spin coating, and HPHT diamond growth. Achieving such precision demands large, high-quality datasets to process dynamics and optimize fabrication parameters. However, experimental data collection is costly, slow, and limited in scale, constraining the development of accurate and generalizable machine learning model. Existing data-driven approaches rely on small, noisy experimental datasets or purely simulation-based data, which often lack physical fidelity and fail to capture process-specific defect mechanisms. To address these limitations, we propose the 85 Physics Rule-Based Synthetic Data Generation Framework, which integrates fundamental manufacturing equations—including Arrhenius, Langmuir, and Meyer-hofer relations—with process-specific defect formation rules. This framework generates over 100,000 physically consistent samples, forming a rich dataset that adheres to underlying manufacturing physics. A Random Forest model trained on this synthetic dataset achieves R2= 0.993 and r = 0.994 against high-fidelity simulation benchmarks, outperforming models trained on limited experimental data. The framework reduces data-collection costs by over 95%, enabling large-scale, physics-consistent simulation validation for under $1,000 in compute resources. Cross-process validation demonstrates that datasets generated with 85 physics rules match experimental data in predictive accuracy across four major quantum fabrication processes. Furthermore, explainability analysis shows 69.8% alignment with established physical principles, confirming that the learned representations remain grounded in real-world manufacturing physics.