wafer-proc-sim: Physics-Informed Machine Learning for SiC Wafer Process Simulation
wafer-proc-sim: Physics-Informed Machine Learning for SiC Wafer Process Simulation
wafer-proc-sim is an open-source framework for physics-informed simulation of silicon carbide (SiC) wafer processing, covering the full semiconductor manufacturing pipeline from front-end dicing and grinding through Fab process equipment models to back-end packaging and test. The framework extends the TMCMC Bayesian inference and multiscale FEM methodology developed in Nishioka et al. (2026, doi:10.5281/zenodo.18790007) to the semiconductor domain, integrating Gaussian process (GP) surrogate models for calibration against experimental dicing data. It includes quantitative physics models for semiconductor equipment manufacturers (ASML EUV, TEL, Disco, Lasertec, Advantest, Lam Research, AMAT, KLA), device manufacturers (TSMC, Intel, Samsung, SK Hynix, Nvidia), and emerging technologies including vertically integrated mega-fabs (Terafab), silicon spin qubit fabrication for quantum computing, and hyperscaler custom AI ASIC supply-chain analysis. All models are validated against peer-reviewed literature with 66 physics-constrained unit tests.
If you use this software in your research, please cite it using the metadata in this file.
- University of Hannover Germany
- Keio University Japan
AI ASIC, physics-informed machine learning, SiC wafer dicing, TMCMC Bayesian inference, multiscale coupling, surface code QEC, Gaussian process surrogate, ASML, hyperscaler, uncertainty propagation, semiconductor process simulation, FEM simulation, Disco, quantum computing, semiconductor equipment, silicon carbide, EUV lithography, Terafab, TEL, semiconductor ecosystem, silicon spin qubit
AI ASIC, physics-informed machine learning, SiC wafer dicing, TMCMC Bayesian inference, multiscale coupling, surface code QEC, Gaussian process surrogate, ASML, hyperscaler, uncertainty propagation, semiconductor process simulation, FEM simulation, Disco, quantum computing, semiconductor equipment, silicon carbide, EUV lithography, Terafab, TEL, semiconductor ecosystem, silicon spin qubit
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!