SECS/GEM + Edge AI: Bringing Intelligence to Equipment Communication

The “Universal Language” of Semiconductor Equipment: SECS/GEM

In a modern fab, hundreds of pieces of equipment from over a dozen different vendors may be operating simultaneously. To enable efficient coordination between these tools and the factory’s Manufacturing Execution System (MES) and Equipment Automation Program (EAP), a unified communication standard is essential.

SECS/GEM is the standard protocol framework established by the semiconductor industry for this purpose:

• SECS (SEMI Equipment Communications Standard): Defines message formats and transmission rules between equipment and host, including SECS-I (RS-232 serial) and SECS-II (message structure)
• GEM (Generic Equipment Model): Built on top of SECS, defines the standard behaviors and state models that equipment should implement, including equipment state management, alarm management, remote control, and data collection
• HSMS (High-Speed Message Services): A TCP/IP transport layer protocol that replaces SECS-I, supporting high-speed network communication

In short, SECS/GEM is the universal language for equipment-to-factory communication — regardless of equipment brand or model, interoperability is achieved through this standard.

Why SECS/GEM Is Critical for AI-Driven Intelligence

AI applications in semiconductor manufacturing — whether Virtual Metrology (VM), R2R automatic tuning, or intelligent equipment diagnostics — all require two foundational capabilities:

• Real-time equipment data acquisition: Sensor parameters, process recipes, equipment states, alarm information, etc.
• Command dispatch to equipment: Parameter changes, recipe switching, remote control, etc.

The SECS/GEM protocol provides exactly these two capabilities. It serves as the “bridge” between AI and semiconductor equipment — without this bridge, no matter how powerful the AI algorithms are, they cannot truly reach the equipment.

Edge Computing: Unleashing the Power of AI at the Equipment Level

In traditional architectures, equipment data must pass through EAP to a central server or cloud for AI model processing. This approach presents several issues:

• High latency: Data transmission and processing queues introduce response delays, failing to meet real-time control requirements
• Data security risks: Sensitive process data leaving the equipment side increases the risk of data leakage
• Complex deployment: Requires modification of existing IT/OT architecture, involving integration across multiple systems

The edge AI approach pushes computing power down to the equipment level — deploying a lightweight AI compute node adjacent to the equipment that communicates directly with the tool via the SECS/GEM protocol. This enables:

• Millisecond-level inference: Data is processed locally with no upload delays
• On-premises data retention: All computation is performed locally, meeting data security requirements
• Non-invasive deployment: No changes to existing MES/EAP architecture — true plug-and-play

SECS/GEM + Edge AI = True Equipment Intelligence

When the SECS/GEM protocol is deeply integrated with edge AI, semiconductor equipment gains the capabilities of perception, analysis, and decision-making:

• Perception: Real-time acquisition of equipment operating data via SECS/GEM
• Analysis: Edge AI models perform feature extraction, anomaly detection, and quality prediction on the data
• Decision-making: Analysis results are converted into control commands and dispatched to the equipment via SECS/GEM

This is the complete closed loop of “equipment intelligence” — equipment is no longer a passive executor but an intelligent node with autonomous optimization capabilities.

MST Semiconductor’s NeuroBox Edge Intelligence Platform features a built-in SECS/GEM protocol stack for direct connection to semiconductor equipment, enabling data acquisition, AI inference, and closed-loop control at the edge — the ideal solution for deploying edge AI on the semiconductor production line.

Related Reading

• How R2R Automatic Tuning Improves Wafer Yield
• Edge AI vs. Cloud AI: How Should Semiconductor Production Lines Choose?
• Intelligent Diagnostics for Semiconductor Equipment: From Reactive Maintenance to Predictive Maintenance