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"escape_risk_proxy": "0.119", "false_hold_risk_proxy": "0.053", "sampling_cost_index": "0.598", "decision_gate": "release_with_boundary", "evidence_state": "teaching_claim_with_guardband", "next_safe_check": "keep_guardband_and_periodic_confirmation" }, { "case_id": "VMET001-2607", "module_context": "ald_dielectric_thickness", "metrology_target": "film_thickness", "target_unit": "nm_equiv", "sample_plan": "adaptive_uncertainty", "sample_plan_note": "sample where uncertainty or drift rises", "data_age": "fresh", "sensor_health": "stable", "physical_metrology_fraction": "1.00", "model_family": "gaussian_process_proxy", "case_index": 2607, "measured_sample_count": 74, "training_rows": 201, "drift_score": "0.400", "sensor_shift_score": "0.030", "uncertainty_sigma_proxy": "0.520", "prediction_error_nm_proxy": "0.320", "guardband_nm_proxy": "1.021", "cpk_proxy": "1.669", "escape_risk_proxy": "0.112", "false_hold_risk_proxy": "0.050", "sampling_cost_index": "0.598", "decision_gate": "release_with_boundary", "evidence_state": "teaching_claim_with_guardband", "next_safe_check": "keep_guardband_and_periodic_confirmation" }, { "case_id": "VMET001-2608", "module_context": "ald_dielectric_thickness", "metrology_target": "film_thickness", "target_unit": "nm_equiv", "sample_plan": "adaptive_uncertainty", "sample_plan_note": "sample where uncertainty or drift rises", "data_age": "fresh", "sensor_health": "stable", "physical_metrology_fraction": "1.00", "model_family": "conformal_guardband_proxy", "case_index": 2608, "measured_sample_count": 74, "training_rows": 201, "drift_score": "0.400", "sensor_shift_score": "0.030", "uncertainty_sigma_proxy": "0.563", "prediction_error_nm_proxy": "0.343", "guardband_nm_proxy": "1.456", "cpk_proxy": "1.665", "escape_risk_proxy": "0.116", "false_hold_risk_proxy": "0.118", "sampling_cost_index": "0.598", "decision_gate": "release_with_boundary", "evidence_state": "teaching_claim_with_guardband", "next_safe_check": "keep_guardband_and_periodic_confirmation" } ] }, "role_views": [ { "view": "operator", "focus": "Confirm sampling plan, calibration state, lot/wafer/site identity, tool health, and data-integrity handoff before trusting a model row." }, { "view": "engineer", "focus": "Compare model family, data age, drift score, uncertainty, prediction error, and guardband before reducing physical metrology." }, { "view": "integration", "focus": "Translate metrology confidence into process-control, device/circuit guardband, release-review, and downstream evidence needs." }, { "view": "yield", "focus": "Read Cpk proxy, escape risk, false-hold risk, and sampling cost together rather than optimizing one number alone." } ], "public_boundary": [ "All rows are original SemiAgora teaching proxies, not wafer fab data, tool logs, private metrology, recipe output, or qualified production evidence.", "The browser filters saved CSV rows only; it does not train a model, run a live solver, connect to equipment, ingest uploads, or execute APC/dispatch logic.", "No process recipe, sampling policy, release rule, quality disposition, foundry claim, product claim, or qualification decision is made.", "Cpk, uncertainty, error, escape risk, false-hold risk, and cost numbers are normalized educational proxies and must not be reused as real process limits.", "No account, upload, payment, comment, newsletter action, job/event workflow, public service intake, or private wafer-data workflow is enabled." ], "methods": [ "Generate a 2880-row factorial teaching table over module context, sample plan, data age, sensor health, physical metrology fraction, and model family.", "Compute measured sample count, training rows, drift score, uncertainty proxy, prediction error proxy, guardband proxy, Cpk proxy, escape-risk proxy, false-hold-risk proxy, sampling-cost index, decision gate, evidence state, and next safe check.", "Use official public source anchors for vocabulary and problem framing only; all visible rows, formulas, labels, and limitations are original SemiAgora teaching content." ], "limitations": [ "The virtual-metrology values are not calibrated to CMP, lithography, etch, implant, ALD, or any other real process module.", "Model-family labels are educational proxies; no machine-learning library, Gaussian-process fit, conformal-prediction package, APC system, or commercial VM tool is run on the public page.", "A real VM program needs owned sensor streams, matched physical metrology, drift monitoring, data lineage, uncertainty validation, process-owner review, and quality governance.", "No live server model training, uploaded wafer data, equipment-control action, or APC execution is enabled.", "This lab teaches evidence discipline; it cannot approve sample reduction, release a lot, replace metrology, qualify a recipe, or set a manufacturing control policy." ], "sources": [ { "label": "NIST virtual metrology dynamic sampling", "url": "https://www.nist.gov/publications/comparative-study-semiconductor-virtual-metrology-methods-and-novel-algorithmic", "note": "Official 2025 NIST publication anchor for virtual metrology, drift/shift, uncertainty quantification, adaptive sampling, and public CMP-dataset validation framing." }, { "label": "IRDS / NIST virtual metrology white paper", "url": "https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=924090", "note": "Official white-paper anchor for virtual-metrology benefits, data-quality issues, confidence metrics, correlation with real metrology, and model-maintenance concerns." }, { "label": "NIST metrology and process-control uncertainty", "url": "https://www.nist.gov/publications/metrology-and-process-control-dealing-measurement-uncertainty", "note": "Official anchor for why measurement uncertainty can create false pass/fail decisions in process-control loops." }, { "label": "NIST CHIPS Metrology Program", "url": "https://www.nist.gov/chips/research-development-programs/metrology-program", "note": "Official program anchor for fit-for-purpose semiconductor measurements as devices and manufacturing stacks become harder to monitor and predict." }, { "label": "NIST/SEMATECH process monitoring handbook", "url": "https://www.nist.gov/publications/nistsematech-engineering-statistics-handbook-chapter-6-process-or-product-monitoring", "note": "Public handbook anchor for SPC/SQC vocabulary and checking whether a process has degraded." }, { "label": "NIST process capability handbook", "url": "https://www.itl.nist.gov/div898/handbook/pmc/section1/pmc16.htm", "note": "Public handbook anchor for Cp/Cpk capability vocabulary and assumptions." } ] }