SEMICONSemiconductor ConventionSEMICON is the name of an international trade show for the semiconductor industry organized by SEMI. West 2026 reflected the industry’s growing interest in autonomous factory operations, with smart-manufacturing discussions tying together digital twins, agentic AIartificial intelligence, physical AIartificial intelligence and automation for facilities. PDF Solutions also planned demonstrations of Exensio Aurora, an analytics architecture intended to support agentic AIartificial intelligence across semiconductor manufacturing operations and the supply chain.
The emphasis, however, shifted quickly from vision to control. An AIartificial intelligence system may correlate equipment signals with yield changes, but that does not by itself justify action on a live production line. A process engineer still has to determine whether the pattern points to an instrument problem, a material-lot anomaly, a maintenance effect or a real process shift before any parameter change is made.
That makes decision-escalation design a prerequisite for autonomous fabs. Permission boundaries need to be explicit: some agents may only retrieve records and draft explanations, while others may be allowed to propose holds or parameter changes. Final authority remains with an authorized engineer or operations leader, and a generic promise to keep humans in the loop is not enough unless the workflow defines the decision, reviewer, evidence requirements and autonomy limits.
Traceability is another requirement. AIartificial intelligence recommendations should be accompanied by the source data, affected tools or lots, known uncertainties and intended effect. The review record should show who evaluated the evidence, which alternative explanations were considered, and whether the recommendation was accepted or rejected. A readable explanation is not a substitute for that underlying record.
The test plan also has to cover exceptions before automation is expanded. Missing telemetry, conflicting process indicators, a tool returning from maintenance, unusual material lots, and apparent improvements followed by downstream quality problems should all be part of validation. Each case should define when automation is suspended, when a second reviewer is required, and who can restart a stopped workflow.
Performance measurement should focus on decisions, not just dashboards. Useful metrics include time to identify and investigate actionable anomalies, the rate of recommendations reversed after review, repeat exceptions and engineering hours spent verifying suggestions. Where possible, those measures should be tied to controlled comparisons for cycle time, scrap and yield.
The workforce implications are equally important. Experienced process engineers may be able to spot plausible correlations that fail under changing production conditions, while junior engineers risk losing practice in reasoning when an agent presents a diagnosis too early. The operational question for autonomous manufacturing is therefore not only how AIartificial intelligence improves analysis, but how fabs prove evidence, accountability and override rights for every agent-initiated action.