WaferYieldIQ

WaferYieldIQ is a web app with an AI-assisted analytics layer that helps semiconductor process and yield engineers triage yield loss faster using messy fab data. It ingests common exports (CSV/Parquet) from MES/SPC/test systems and correlates wafer maps, parametric test, lot history, tool/chamber, recipe, and time windows. The app auto-builds a “suspect list” of top contributors (tools, steps, chambers, recipes, operators, shifts) and surfaces statistically defensible evidence (effect sizes, confidence, multiple-hypothesis correction) instead of hand-wavy charts. It also generates a reproducible investigation report engineers can share with manufacturing, including recommended next experiments (split lots, hold lots, tool matching). This is not a magic black box: it’s a structured workflow that reduces time-to-root-cause and prevents repeated firefighting. Expect integration work and data wrangling to be the real cost driver, not the UI.