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kyb8801/README.md

Hi, I'm Youngbum Kim πŸ‘‹

Metrology Γ— AI Γ— Uncertainty Quantification

Optics Ph.D. with 8+ years in optical & semiconductor metrology (SEM, AFM, Raman, NSOM), building AI applications that don't just predict β€” they report how much you can trust the prediction, with GUM-compliant uncertainty budgets (JCGM 100/101).

  • πŸ”¬ Measurement scientist first: ISO 18516:2019 round robin β€” Korean-side participant, first author of the resulting paper (Curr. Appl. Phys. 20, 71–77, 2020) Β· practitioner at a KOLAS-accredited ISO 17034 reference-material producer
  • πŸ“ I build the full loop: physics forward model β†’ inverse solver β†’ GUM uncertainty budget
  • 🧰 Domains: OCD / scatterometry, XRR, SEMΒ·TEM image analysis, AFM probe health, Raman/SERS QC
  • πŸ€– MCP builder: author of an MCP server for GUM measurement uncertainty β€” a prior-art search (2026-07) found no equivalent, which is not the same as being first
  • πŸ§ͺ 4 solo patent applications in metrology & inspection AI (KR, 2026)
  • 🌱 Upstream contributor: conformal prediction (CQR) merged into torch-uncertainty (PyTorch UQ framework, β˜…500+)

πŸš€ Featured Projects

Project What it does Stack
metroai KOLAS Compliance OS β€” MCP server + 6 AI agents + GUM/MCM/QMC measurement-uncertainty engine + Ed25519-signed audit trail, for ISO/IEC 17025 accredited labs. 243 automated tests, CI. Python, MCP
metrology-inverse Forward β†’ inverse β†’ GUM uncertainty across 3 instruments: OCD (RCWA), XRR (Parratt), autodiff CD fitting β€” validated on real NIST scatterometry data (L100P300, 9 dies). Exact-Jacobian sensitivity for the uncertainty budget. Python, PyTorch, Meent, refnx
acoustic-resonance-tomography 3D buried-defect detection & material ID in semiconductor BEOL via GHz acoustic resonance β€” physics simulations (FDTD/TMM) + PINN inverse scattering. Patent pending (KR 10-2026-0109370). Python, PyTorch
measurement-uncertainty-mcp MCP server for GUM uncertainty analysis: Type A/B, Welch-Satterthwaite Ξ½_eff, expanded U(k), JCGM 101 Monte Carlo, KOLAS-ready budgets. 44 tests, live on MCPize. Python, MCP
tiphealth (private β€” patent pending KR 10-2026-0129260) HAR AFM tip predictive maintenance β€” image ML at a reference recipe + Archard-model recipe scaling, 8 probe models Γ— 6 materials (simulation-based), conformal prediction intervals, Dockerized API. Python, Docker, Streamlit
spectraguard Uncertainty-aware spectral QC for SERS/Raman/IR β€” 6-metric confidence score with bootstrap CIs, cross-instrument transfer, streaming SPC, CLI + CI pipeline. Python, NumPy, SciPy
semiconductor-defect-classifier Defect classification on SECOM fab sensor data (1,567 wafers Γ— 590 sensors, 6.6% defect rate) β€” imbalance handling, Optuna tuning, honest K-fold evaluation. Python, XGBoost, Optuna
semiconductor-ai-portfolio Analysis pipelines on my own PhD measurement data: MoSeβ‚‚ photoluminescence peak/FWHM analysis, NSOM defect mapping, TMD comparison. Jupyter, pandas, SciPy

πŸ”Œ MCP & Open Source

Model Context Protocol servers I built and maintain:

πŸ›  Tech

Python PyTorch XGBoost Optuna NumPy SciPy pandas Jupyter Streamlit Docker GitHub Actions

Metrology & standards: GUM (JCGM 100:2008) Β· JCGM 101 Monte Carlo Β· ISO/IEC 17025 Β· ISO 17034 Β· RCWA Β· XRR Β· SEM/TEM Β· AFM Β· Raman/SERS Β· NSOM

πŸ“ How I report numbers

Every figure in these repos ships with its data origin β€” synthetic, live, or stub β€” and no number appears in a README unless it can be reproduced from the repo. Some consequences of that rule are kept visible on purpose:

  • spectraguard Figure 5 is labelled not a benchmark: its ground-truth labels come from the same synthetic generator whose parameters the tool measures, so the comparison is circular by construction and the caption says so instead of quoting the AUC.
  • semiconductor-defect-classifier documents that its demo previously returned a constant probability across all 7,776 slider combinations, why (SimpleImputer only replaces NaN; the input was np.zeros), and what replaced it.
  • Prior-art searches are quoted with their date, registry and search terms β€” never as "world first".

A measurement you can't reproduce isn't a measurement.

πŸ“« Contact

πŸ“§ kyb8801@gmail.com

Pinned Loading

  1. acoustic-resonance-tomography acoustic-resonance-tomography Public

    3D buried-defect detection & material ID in semiconductor BEOL via GHz acoustic resonance β€” physics simulations (FDTD/TMM) + PINN inverse scattering. Patent pending (KR 10-2026-0109370).

    Jupyter Notebook 1

  2. measurement-uncertainty-mcp measurement-uncertainty-mcp Public

    MCP server for GUM (JCGM 100:2008) measurement uncertainty - Type A/B, Welch-Satterthwaite, coverage factor k, Monte Carlo per JCGM 101. For ISO/IEC 17025 and ISO 10012:2026 labs.

    Python

  3. metroai metroai Public

    KOLAS Compliance OS β€” MCP server + 6 AI agents + GUM/MCM/QMC measurement uncertainty engine + Ed25519-signed audit trail. For ISO/IEC 17025 accredited labs.

    Python

  4. metrology-inverse metrology-inverse Public

    OCD (RCWA) Β· XRR forward–inverse pipelines with GUM uncertainty budgets β€” validated on NIST scatterometry reference data

    Python

  5. spectraguard spectraguard Public

    Uncertainty-aware spectral quality assessment for SERS, Raman, and IR spectroscopy. 6-metric confidence scoring with bootstrap CI.

    Python