Four engineers with direct experience in defect inspection, process tool data, and yield analytics at specialty 200mm fabs. We built Wafertune because we had each spent years working around the data alignment problem that makes excursion diagnosis slower than it needs to be.
CEO & Co-Founder
Jonas spent six years as an applications engineer at KLA Instruments, supporting defect inspection deployments at 200mm fabs across the US Southwest. He holds a BS in electrical engineering from Arizona State University. That field experience — and a six-week root-cause investigation at a Phoenix-area power fab — became the direct basis for Wafertune.
CTO
Hiroshi spent five years at Lam Research building tool data export pipelines and analytics integrations for etch and deposition process lines as part of the INSIGHT data platform team. He is based in Phoenix. His familiarity with how chamber parameter data is structured and exported from Lam systems is foundational to Wafertune’s process correlation engine.
Head of Yield Analytics
Svetlana worked for seven years as a process integration and yield engineer at ON Semiconductor’s 200mm power device fab in Glendale, AZ. She understands the day-to-day workflow of a yield engineering team — what outputs are useful, what the excursion closure process looks like inside a fab quality system, and where manual cross-referencing bottlenecks the most time.
ML Engineer
Kweku holds an MS in statistics from Arizona State University, where his research focused on spatial defect pattern classification in semiconductor inspection data. He owns Wafertune’s signature library and pattern-matching model — the component responsible for matching incoming wafer maps against catalogued defect signature templates and returning ranked confidence scores.
When you reach out to Wafertune, you get the engineering team. We talk through your data formats, your tool stack, and what a pilot would look like at your specific fab — not a generic demo.