Rulnix Intelligence connects wafer inspection, equipment telemetry, process data, yield records, and final chip-testing results to help semiconductor manufacturers detect defects, predict failures, investigate root causes, and prevent production losses.
Designed for the engineering teams responsible for yield, equipment reliability, process control, testing, and manufacturing quality.
Rulnix ingests fragmented factory data, reasons over it with five connected intelligence modules, and delivers traceable decisions engineers can act on — with measured yield impact.
AI-powered wafer-defect detection, classification, and spatial-pattern recognition from inspection imagery.
Wafer-lot risk scoring, yield prediction, and production-loss estimation across lines and process steps.
Predictive maintenance and equipment-health monitoring across vacuum, pressure, and cycle-time systems.
Explainable root-cause investigation across wafers, tools, processes, maintenance, and test failures.
Chip-testing analytics, failure clustering, and traceability from final test back to wafer origin.
The five Rulnix modules are not separate applications — they operate as one connected system, passing evidence from detection through investigation to measured improvement.
Detects pressure instability in Etching Tool 4.
Identifies a new contact-defect pattern on inspection.
Predicts production loss for affected wafer lots.
Detects related chip failures at final test.
Identifies the probable root cause with evidence.
Engineers implement the corrective action.
Rulnix measures whether yield actually improved.
Metrics below are illustrative — actual results depend on fab configuration and data maturity.
Yield declined across one production line without a clear explanation, and engineering teams lacked a fast way to connect inspection, equipment, and test data to investigate.
Connect inspection, equipment, yield, process, and testing data through one enterprise AI platform built for semiconductor manufacturing.