What this category of data looks like
Data for this category typically comes from wafer fab MES systems, equipment FDC modules, defect detection and metrology equipment, wafer map databases, process recipe libraries, and experimental data. Daily documents include process flow cards, batch run records, equipment recipes, SPC charts, exception handling sheets, defect atlases, and yield reports. Data update frequencies cover each wafer batch transfer, real-time parameter reporting, and daily summary statistics. Most fields include nanoscale process parameters, batch IDs, defect coordinates, yield-related parameters, and units such as micrometers, watts, and batch numbers.
What constraints these characteristics impose on HTTP Interfaces and External Systems
Data sources for this category are scattered, and update rhythms vary widely. Some parameters require real-time reporting, while others require batch-level aggregation. As a result, HTTP interfaces must adapt to request and push scenarios with multiple frequencies. Data includes complex fields such as nanoscale process parameters and defect spatial coordinates. Interfaces must support nested request bodies and field validation rules. Wafer batches have strong cross-departmental association attributes. Interfaces must use batch ID as the core association parameter to avoid data mismatching. At the same time, data anomalies will directly lead to wafer batch scrapping. Interfaces must include built-in idempotency and failure retry mechanisms to ensure data transmission reliability.
How to Configure
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Authorization | Bearer <FastGPT-issued access token> | Complies with HTTP interface authentication specifications and adapts to existing wafer fab system authentication processes |
| Request timeout | 30–60 seconds | Real-time parameter delay tolerance in wafer manufacturing is low. Excessive timeout blocks process flow |
responseData parsing rules | Sort by batch ID and collection timestamp in ascending order | Allows process teams to quickly locate associated historical data for abnormal batches |
| Bulk data submission limit | First 200 entries | Process records for a single wafer batch typically fall within this range, avoiding excessive interface load |
variables injection configuration | Bind wafer batch ID and core process parameters | Accurately matches contextual data required for supplier and quality audits |
Three Common Configuration Mistakes
- Symptom: An HTTP interface call returns
500 Internal Server Error, and SQL logs show insufficient permission errors. Cause: Database connection permissions for external systems were not reconfigured after restarting the Docker container or server, causing FastGPT to fail to read core wafer manufacturing data. - Symptom: When submitting wafer batch data in bulk, the interface does not return data IDs and local mapping fields. Cause: The configuration item to enable
responseDataID return was not included in the request, making association between local data and platform data impossible. - Symptom: Interface calls time out, and monitoring shows request duration exceeds the preset threshold. Cause: Request timeout was not set appropriately, and retry mechanisms were not enabled, blocking real-time process parameter transmission.
How to Verify a Successful Configuration
- Call the test interface, pass a simulated wafer batch ID, check if
Authorizationauthentication passes, and confirm the returnedresponseDataincludes expected process parameters and batch information. - Configure
variablesbinding test parameters, initiate a bulk data submission request, and verify returned results are parsed per preset rules and associated with batch IDs. - Simulate permission verification failure or timeout scenarios, and check if the system triggers preset alert and retry logic.
- View external system data synchronization logs to confirm wafer manufacturing batch records have been synchronized to the FastGPT platform at the configured frequency.
The values provided are common starting points and should be evaluated against local operational samples.