SECS/GEM + Edge AI: Bringing Intelligence to Equipment Communication
Key Takeaway
SECS/GEM + Edge AI creates an intelligent equipment communication layer. SECS/GEM provides the data pipeline (equipment parameters, process data, alarms), while edge AI adds prediction and control capabilities. NeuroBox sits between EAP and equipment as the AI brain.
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.
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