Semiconductor manufacturing enterprises operate in a highly competitive, precision-driven environment where quality management, traceability, and regulatory compliance are of paramount importance. One such enterprise faced challenges in wafer tracking and lot traceability across multiple fab and test stages. Manual processes, fragmented systems, and delayed reporting hindered their ability to quickly analyse failures, enforce quality controls, and meet compliance standards.
To address this, the enterprise deployed Agentic AI solutions within a context-first agentic infrastructure. Agent labels logs every wafer or batch movement in real-time. Agent search retrieves historical tracking and current production data, and Agent analysts analyse trends, deviations, and failures. This solution provides end-to-end visibility, ensures regulatory compliance, and accelerates root-cause analysis, enabling operational efficiency and data-driven manufacturing.
By implementing these agentic AI platforms, the organisation achieved digital traceability, reduced operational risks, improved audit readiness, and gained actionable insights into process optimisation and quality assurance.
The customer, a semiconductor manufacturer, struggled with fragmented lot tracking, manual reporting, and slow failure analysis.
Key business problems included:
Limited traceability: Wafer/batch history was siloed across fab and test systems.
Delayed compliance reporting: Manual data aggregation slowed regulatory submissions.
Slow failure analysis: Identifying root causes for defective wafers required significant time and manual effort.
High operational risk: Incomplete traceability increases the risk of recalls or non-compliance.
Business goals:
Ensure full traceability of each wafer/batch from start to finish.
Accelerate failure analysis and quality control processes.
Support regulatory compliance with auditable records.
Improve workflow efficiency and data reliability across production systems.
Existing solution limitations:
Manual tracking is prone to errors.
Disconnected logs across fab, test, and ERP systems.
Limited ability to analyse historical trends or predict failure points.
Time-consuming audit preparation.
Multiple fab/test systems generating heterogeneous data.
Disconnected databases with inconsistent logging formats.
Manual aggregation of historical lot data.
Integration and Data Management Issues
Scalability, Reliability, and Performance Limitations
Existing systems were unable to track thousands of wafers in real-time.
Slow queries and high latency during historical data retrieval.
No real-time visibility into process deviations or batch anomalies.
Security and Compliance
No end-to-end audit trail for wafers/batches.
Access to batch history is not role-controlled.
Compliance reporting required extensive manual verification.
The company implemented Agentlabel.ai, Agentsearch.ai, and Agentanalyst.ai to enable end-to-end traceability and lot tracking.
Agent label: Logs wafers/batches at each fab and test step.
Agent search: Retrieves historical and current batch data for inspection, audits, or analysis.
Agent analyst: Analyses trends, identifies failures, and provides actionable insights for quality assurance.
These agents work together to:
Auto-log process steps and updates for every batch.
Retrieve complete wafer/batch histories in seconds for audits or failure analysis.
Analyse process deviations, defect trends, and potential compliance risks.
Generate summary reports for management and regulatory purposes.
|
Industry |
Use Cases |
Value Delivered |
|
Semiconductors & High-Tech |
Wafer fabs, cleanroom robotics |
Full traceability, faster failure analysis, and regulatory compliance |
|
Pharmaceuticals |
Batch production lines |
Auditable batch records, QA compliance |
|
Food & Beverage |
Production and packaging lines |
End-to-end lot tracking, recall prevention |
Agent label → Logs batch/process steps in real time.
Agent search → Fast retrieval of historical and current batch data.
Agent analyst → Analyses deviations, failures, and process trends.
Analysis & Decision Support
Workflow
Data
Business Benefits:
Reduced time for batch traceability and audit reporting by 60%.
Faster failure analysis, enabling quicker corrective actions.
Improved regulatory compliance with auditable batch histories.
Minimized risk of recalls or non-compliance.
Technical Benefits:
Real-time batch logging across multiple fab and test systems.
Scalable retrieval of historical wafer/batch data.
Automated analysis and reporting for quality teams.
"Deploying Agent label, Agent search, and Agent analyst has transformed our batch tracking and quality control. We can trace any wafer in seconds, reduce audit prep time, and quickly identify root causes of defects. The system is a game-changer for compliance and efficiency."
End-to-end traceability requires integrating heterogeneous production systems early.
Accurate logging depends on process discipline and operator training.
Centralised analytics accelerates compliance and quality assurance.
Continuous feedback improves process and analysis models over time.
Start with critical wafers/batches before scaling plant-wide.
Implement role-based access to batch history data.
Maintain automated audit logs for regulatory compliance.
Keep feedback loops between process engineers and AI outputs.
By deploying Agent label, Agent search, and Agent analyst, the semiconductor manufacturer achieved full wafer/batch traceability, accelerated failure analysis, and improved compliance reporting. The solution reduces operational risk, increases workflow efficiency, and positions the enterprise for advanced, data-driven manufacturing practices.