Wafertune correlates defect inspection data, chamber process logs, and metrology exports from the tools already running in your specialty 200mm fab — and delivers a ranked root-cause hypothesis to the yield engineer's queue within 2–4 hours of excursion detection.
The average time a specialty 200mm fab spends diagnosing a single defect excursion to root cause using manual CSV cross-referencing.
Wafertune delivers a ranked root-cause hypothesis card to the yield engineer's queue within 2–4 hours of excursion detection.
Estimated annual yield loss from delayed root-cause closure at a typical specialty 200mm power or MEMS fab running 8–20 excursion events per quarter.
Wafertune's signature library covers 200+ labeled defect patterns — edge-ring, center-cluster, radial-spoke, scratch-linear, and more — built from real 200mm fab inspection data.
When a wafer map arrives from KLA or Onto, Wafertune runs spatial correlation against a library of 200+ labeled defect signature templates — edge-ring, center-cluster, scratch-linear, radial-spoke, and more — and returns the top three matching signature types with confidence scores in under 90 seconds. Yield engineers start each investigation with a narrowed hypothesis set rather than a blank wafer map, and each signature match links directly to the process steps and chambers historically associated with that defect type.
Wafertune pulls chamber process parameter logs from Lam Research and Applied Materials tool data layers and aligns them in time-series with the wafer map inspection events for the same lot sequence. When a defect excursion correlates with an etch rate drift or deposition uniformity deviation on a specific chamber, the platform flags the chamber and step combination and surfaces the parameter trace alongside the defect map in a single review screen — eliminating the manual cross-referencing step that typically consumes 1–2 days per excursion cycle.
Wafertune reads Synopsys Camelot SPC violation events via standard export and merges them into the defect correlation timeline. A control chart violation on etch uniformity that precedes a defect excursion by 4–6 hours becomes evidence in the root-cause card rather than a separate engineering ticket. Yield engineers who already run Camelot stop treating SPC review and defect investigation as two disconnected workflows.
Wafertune works with the data export formats already produced by the tools in your fab. No new sensors, no parallel data pipelines, no separate engineering project to stand it up.
Wafertune ingests defect wafer maps from KLA and Onto systems, chamber parameter traces from Lam and Applied Materials tools, and metrology and SPC exports from Inficon and Synopsys Camelot. Standard export formats, no custom integration work required from your IT team.
Each incoming wafer map is matched against the 200+ signature library. The pattern matcher runs in under 90 seconds and returns the top matching defect signature types with confidence scores, narrowing the investigation before any manual analysis begins.
Wafertune aligns the defect signature with chamber parameter logs from the same lot sequence, identifying whether the matched defect type correlates with a specific etch rate deviation, deposition uniformity drift, or SPC control chart violation on a particular chamber and recipe step.
A ranked root-cause hypothesis card arrives in the yield engineer's queue within 2–4 hours of excursion detection. It shows the matched defect signature, the correlated chamber parameter trace, a confidence score, and a recommended corrective action — ready to route to the process engineering team or fab manager.
A typical specialty 200mm power or MEMS fab handles 8–20 defect excursion events per quarter. Each one requires a separate diagnostic cycle that pulls yield engineers off other work for 3–7 days while affected lots continue moving through the line.
Each unresolved excursion erodes 0.5–2.5% gross yield across affected lots. That loss accumulates across the quarter. At the high end of excursion frequency and yield impact, the annual cost to a single fab reaches into the millions.
Defect inspection data (KLA/Onto), chamber process logs (Lam/Applied Materials), and metrology exports (Inficon/Synopsys Camelot) live in three separate proprietary systems with no automated correlation layer. Wafertune is that layer.
We work directly with yield engineering teams at specialty 200mm fabs. A demo starts with your data formats and your tool stack — no generic slide deck.