SynthKernel's AI analyzes inline inspection data from CDSEM, optical wafer scanners, and e-beam tools to pinpoint yield-killing defect clusters — run-to-run, in real time.
SynthKernel integrates directly with your existing process control systems to surface actionable yield intelligence without adding integration overhead.
Classify defect types — bridging, particle, pattern, and scratch — across multiple inspection layers using trained convolutional models, reducing misclassification rates compared to rule-based bins.
Cross-correlate defect maps from optical review, e-beam, and electrical test to isolate the specific process step responsible for a yield excursion — not just the layer where it appears.
Connect to APC loops and SPC charts to flag process drift within minutes of a control chart violation, before the affected lots advance to the next critical layer.
Track each wafer's full process history — etch, deposition, CMP, lithography — and link inspection data to the exact equipment ID and recipe version that touched it.
When a yield event occurs, SynthKernel generates a ranked list of probable root causes with supporting evidence drawn from equipment logs, metrology data, and lot history.
All models and data run inside your facility network. No wafer images, die maps, or process parameters leave your fab. Full audit log for every inference event.
SynthKernel ingests KLA, Lam, ASML, and Applied Materials inspection output via standard SEMI E10 and SECS/GEM interfaces. No custom adapters required for most installed toolsets.
Defect images and metrology readings are scored by trained AI models. The correlation engine links electrical test (WAT, probe) results back to specific defect signatures at the wafer-level.
Yield engineers receive ranked alerts with supporting evidence — die map overlays, equipment logs, and process history — prioritized by estimated yield impact, not detection order.
Defect classification accuracy on CDSEM review images across 14nm and below nodes
Average inference latency per wafer map, running on-premise with no cloud dependency
Median time from inspection event to yield alert delivered to the responsible engineer
Typical reduction in time-to-root-cause during excursion events in pilot deployments
Semiconductor yield problems are expensive because they're hard to locate. These are the workflows where AI-assisted analysis changes the economics.
Identify yield-limiting defect classes in leading-edge logic and DRAM nodes where pattern complexity makes manual review impractical at production volumes.
Accelerate tool qualification and chamber matching by detecting statistically significant differences in defect signature between qualified and candidate chambers.
When a process excursion is detected, automatically pull all lots processed on the suspect tool or with the affected recipe and quantify estimated yield impact before disposition.
Request a demo and see how SynthKernel integrates with your existing inspection toolset in under two weeks.