Dry Pump Communication Converter for SECS/GEM Protocol Translation
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Solution Overview
Problem
The existing dry pump monitoring systems face challenges in real-time data transmission due to varying sensor data formats from different manufacturers, leading to delays and potential data loss, which hinders accurate and timely monitoring in semiconductor manufacturing processes.
Innovation Solution
A communication converter with a first communication module, a memory module, and a processing module that interprets and transcodes sensor data from dry pumps into standardized protocols like Modbus, SECS/GEM, Modbus TCP, and MQTT, enabling direct transmission to monitoring hosts and cloud servers for real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sensor data from multiple dry pump manufacturers with different coding formats are collected through a centralized SCADA server, then data collection capability is improved, but data transmission delay increases and real-time monitoring capability deteriorates
Solution Approach 1:
The system segments the centralized monitoring function by deploying edge computing devices at each dry pump location. These edge devices independently transcode sensor data from different manufacturer formats to SECS/GEM protocol locally, eliminating the need for centralized transcoding and reducing data transmission delay while maintaining multi-format compatibility.
Solution Approach 2:
The edge computing devices perform preliminary transcoding action at the data source before transmission. By converting sensor data to the standard SECS/GEM protocol format at the pump level rather than waiting for centralized processing, the system prepares data in advance, reducing latency and enabling real-time monitoring.
2Adaptability or versatility
If sensor data is transmitted through multiple transcoding stages in a centralized architecture, then compatibility with different manufacturers is improved, but data loss risk increases and system reliability deteriorates
Solution Approach 1:
The transcoding function is extracted from the centralized SCADA server and relocated to edge computing devices at each pump location. This eliminates multiple transcoding stages and intermediate data handling, reducing data loss risk while maintaining compatibility with different manufacturer formats through local edge-device-based conversion.
3Quantity of substance
If a centralized SCADA server collects data from hundreds to thousands of dry pumps, then system coverage is improved, but data transmission complexity increases and error rate worsens
Solution Approach 1:
The system segments the data transmission path by distributing edge computing devices across multiple pump locations. Each edge device independently handles protocol conversion and local data processing, breaking down the complex centralized transmission into simpler distributed connections, thereby reducing overall system complexity and error rates while maintaining broad coverage.
Data Source
AI summary
Provided is a communication converter of a dry pump, including: a first communication module, a memory module, a second communication module, and a processing module. The first communication module receives a sensor data packet from a communication port of the dry pump. The memory module has stored a program that can interpret the sensor data packet, an access address and an identification code of a sensor data. The second communication module is connected to a monitoring host via a first network. The processing module interprets the sensor data packet to retrieve the sensor data, transcodes a coding of the sensor data according to a Modbus protocol, and compiles the transcoded sensor data to generate a data packet of SECS/GEM protocol. The second communication module transmits the data packet of SECS/GEM protocol to the monitoring host.