Validation &
Precision
Validation here means route-backed benchmarks, solver cross-checks, registry reproducibility, and explicit calibration lanes. Physical coupon/VNA truth is still the next major unlock, but the current proof stack is already bound to generated artifacts rather than hero copy.
Open buyer app →External truth, four solvers, calibrated uncertainty.
Eight buyer-shippable artifacts ship live: IEEE-published HFSS-coaxial reference witness, Palace 0.16.0 per-case witness (real transient FEM cross-physics, n=100 multifreq), deep ensemble + OOD flag with per-regime temperature scaling, 60-case route-backed signoff (60/60 RF pass), frequency-resolved S21 recovery from the EM Isolation Compiler, GDS-to-yield bondability framing, promotion CI gate (4 retrain candidates rejected), and a single hashed buyer DD packet. Provenance: the IEEE 6-paper Z₀ truth lane consists of HFSS-coaxial extractions from each paper's published figures/tables (source_type: simulation), not VNA measurement. Real VNA campaign queued (~$200-500K wet lab). Every number bound to a SHA-256-hashed JSON in the repo.
source_type:simulation per measurements.json — not VNA). ML inherits at 6.66% mean / 17.00% max./v1/glass-pdk/predict-impedance with a learned calibration head (MLP, tanh-bounded log_T) trained no-leakage on a 16K cal + 4K val + 8K test 3-way split. Deployed pooled 0.79% ± 0.23%, HBM4 1.30% ± 0.16% across 5 partition seeds on an 80K test slice. All 7 regimes < 5% gate; @95% nominal coverage 0.9545. A 32-config sweep across hidden dimensions, calibration sizes, and feature sets — plus Bayesian, Student-t, and conformal alternatives — confirmed the deployed architecture is at its budget-bounded optimum. Each call returns Z₀ + 95% CI band + OOD flag.promote as screening.POST /v1/photonics/validate-against-ieee. Scope: the AI surrogate at ≥ 99% R² vs our reference solver closes this gap on 5 of 6 primitives (higher-fidelity refresh on the roadmap). See Trust & Validation and ChipletOS Photonic Signoff.POST /v1/glass-pdk/geometry-from-target ships target Z₀ → recovered (d, p, t). Surrogate path: PyTorch autograd through the 3-seed ensemble + Adam over (log d, log p, log t) with regime-feasibility envelope projection and the BEM-warning rule p ≥ 1.55·d. Optional ?refine=adjoint hands the surrogate optimum to a real adjoint-BEM gradient-descent stage (central-difference gradients through the multiconductor BEM forward). Cross-physics witness: r_pooled = 0.99984 over 20 random geometries between BEM-FD gradient and PyTorch autograd through the surrogate (target r ≥ 0.95). All 3 components (∂Z/∂d, ∂Z/∂p, ∂Z/∂t) above 0.96. 8/8 smoke cases converge under 2% Z₀ tolerance with mean cross-physics disagreement 0.77% of target. Live in browser at /playground → "Design from Spec" tab. Witnesses: benchmarks/sprint34/adjoint_bem_vs_surrogate_gradient_witness_2026_04_29.json,benchmarks/sprint34/inverse_design_endpoint_smoke_2026_04_29.json.Package RF Pass
Current flagship `package-signoff` benchmark across 25 named HBM4, UCIe, PCIe Gen6, 400G/800G, and 77 GHz cases.
Mean Worst Z0 Error
Route-backed signoff bundle metric with Touchstone, report, manifest, checksum, and provenance outputs.
Validated S2P Assets
Manifest-backed registry with deterministic sample open/parse/hash validation and authenticated API count parity.
Calibration CI Reduction
Bondability Pipeline measured-anchor signoff lane after ten posterior updates, keeping absolute fab-yield claims gated.
Tests are Truth
Empirical data takes precedence over theoretical simulations. Hardware truth remains separated from solver and surrogate evidence until measured coupon data lands.
Red-team Visibility
Continuous adversarial stress testing on routing and thermal boundaries to detect edge-case failures.
Cross-checks
Witness lanes are bounded and named. FastHenry2, Palace FEM, and OpenEMS each carry explicit scope.
Experiments
Physical coupon work is still next, but the current benchmark layer already includes package-signoff, registry validation, and measured-calibration evidence.
Automated Test Footprint
Our infrastructure executes hundreds of regression tests per build cycle, focusing on high-density suites for advanced packaging constraints.
Glass PDK Suite
Bondability Metrics
Bondability FNO Model
Measured on 20,000 held-out test samples spanning the full operational parameter range. Screening-grade, used to identify high-risk regions before full physics verification.
Training Data Provenance
Derived from 15.92M+ impedance database rows across the unified solver stack.
Model Card: #FNO-992-B
Optimized for Fourier Neural Operators focusing on multi-physics thermal-mechanical coupling.
Electromagnetic Validation
Comparative analysis of signal integrity across heterogeneous substrates.
Glass PDK / BEM Impedance
Structural Strength
Superior modeling of surface roughness and plating skin-depth effects in high-aspect TGVs.
Validation Stack Cross-Checked
IEEE-5 HFSS-coaxial reference MAE is 3.57% (HFSS-coaxial extractions, not VNA); Palace FEM raw outputs preserved at 10.07% median |ΔZ%|.
BEM v5 ML Replay: R² = 0.9520
Diagnostic on 300K v5 parquet rows: row-random replay R² = 0.9520, geometry-challenge R² = 0.9516. Existing checkpoint, not a group-trained holdout. The strict geometry-group aggregate scores mean R²0.9751 / 4.59% mean MAPE across four baseline 1.5M-row runs, with HBM4 at 0.9093 / 8.07%. Expansion lane now totals 901K separately versioned strict rows across the buyer regimes.
Software Limitation
BEM assumptions may diverge in ultra-dense weave patterns above 110GHz without heuristic correction.
IsoCompiler / Audit Artifacts
Three converged solvers
IEEE-published HFSS-coaxial references, Palace 0.16.0 transient FEM (real cross-physics, n=100 multifreq), and FastHenry2 inductance cross-check converge on Z₀ across the buyer-regime corpus. (IEEE Z₀ refs are HFSS sims per measurements.json::source_type, not VNA.)
Software Limitation
High computational cost; currently restricted to localized tile-based verification for 4nm nodes.
Lab-Ready Templates
Pre-configured Statement of Work (SOW) packages for rapid correlation between ChipletOS digital twins and physical lab measurement.
SAM Imaging
Scanning Acoustic Microscopy correlation for void detection in hybrid bonding interfaces.
- > Ultrasonic Calibration
- > Interface Mapping
- > Void Volume Ratio
VNA Correlation
Vector Network Analyzer templates for S-parameter extraction up to 220GHz.
- > De-embedding Logic
- > Multi-port Characterization
- > Return Loss Profile
CHF Experiment
Critical Heat Flux testing protocols for high-power AI accelerators using glass cooling.
- > Thermal Ramp Control
- > Micro-fluidic Sync
- > Pressure Drop Delta
Solver Maturity Matrix
| Component Solver | Key Metric | Status | Production Status |
|---|---|---|---|
| BEM TGV Impedance | 3.57% MAE / 5.75% RMSE vs IEEE-published HFSS-coaxial refs (18 pts; not VNA) · strict aggregate R² 0.9751 / 4.59% MAPE · BEM mesh 0.087% (16×) · 9/10 BEM regime <2% @ 1–250 GHz · Palace 10.07% @ 5 GHz · 901K versioned strict additions | IEEE 18-point extended HFSS-coaxial cross-reference (3.57% MAE / 5.75% RMSE; source_type:simulation per measurements.json — not VNA) + FastHenry2 inductance cross-check; strict geometry-group aggregate across four baseline 1.5M-row runs gives mean unseen-geometry R² 0.9751 at 4.59% mean MAPE, with HBM4 remaining the soft regime at 0.9093 / 8.07%; BEM mesh-independent 0.087% across 16× refinement; 9/10 validation geometries stay below 2% across 1–250 GHz (worst case <5%); Palace 0.16.0 transient FEM at 5 GHz — 10 of 50 sims within ±5% on d=40-50/p=120-150/t=200 regime (median 10.07%); multifreq Palace 100/100 across {28, 77, 110, 200} GHz (real cross-physics truth-cross-check); strict buyer-regime additions total 901K separately versioned rows; novel diff-pair law Z₀_diff ∝ (sep/d)^0.338 | Enterprise Ready |
| TMM RF Isolation | 10⁻¹⁶ energy conservation | Machine-epsilon | Enterprise Ready |
| ILC Zernike Controller | 982/1000 (synthetic MC) | vs 5 baselines (analytical plant; hardware validation pending) | Production |
| Kirchhoff FEM | sub-4ms latency | Warpage prediction | Production |
| IsoCompiler Adjoint | r = 1.0 vs finite-difference | 0/20 alternatives beaten | Production |
| FNO Yield Model | R² = 0.50 pixel / 0.63 image-max | Screening-grade risk prediction | Production |
| LBM Thermal Solver | 720,000x speedup | Analytical benchmark | Enterprise Ready |
| Lot Intelligence Report | 4 signals → 1 release verdict | IPC-A-610G + SEMI E10 + ISO 9000-3 + WM-811K | Production |
| Provenance Lineage API | 13 edges, 10 solvers | Solver + dataset + validation + evidence JSON | Production |
BEM Solver vs Published IEEE Literature (HFSS-coaxial extractions; not VNA — see provenance disclosure)
| Paper | Glass | Published Z₀ | BEM Z₀ | Error |
|---|---|---|---|---|
| Sukumaran et al. ECTC 2014 | Eagle XG | 48.0 Ω | 51.02 Ω | +6.29% |
| Watanabe et al. ECTC 2019 | AF32 | 44.0 Ω | 43.50 Ω | -1.14% |
| Shorey et al. JMS 2016 | Borosilicate | 36.5 Ω | 36.51 Ω | +0.03% |
| Tummala et al. JEP 2020 | EN-A1 | 34.0 Ω | 34.32 Ω | +0.95% |
| Hwang et al. TMTT 2017 | Quartz | 41.0 Ω | 37.13 Ω | -9.44% |
| Mean Absolute Error | 3.57% | |||
Full Evidence Package Under NDA
Complete validation methodology, reproducibility scripts, raw benchmark data, training data provenance, and per-solver risk assessments are available in the NDA data room.