What the data for this category looks like
Technical documentation cross-review for semiconductor wafer fabrication covers data sources including MES systems, equipment FDC collection modules, defect detection and metrology devices, process recipe libraries, and experimental databases. Daily documentation includes process flow cards, batch run records, equipment recipes, SPC control charts, exception handling sheets, defect maps, yield reports, and more. Data updates follow real-time uploads with each wafer batch transfer, real-time synchronization of equipment operating status, and daily summary archiving. Document fields include nanoscale process parameters, batch unique identifiers, defect spatial distribution coordinates, yield ratios, and similar items. Units include nanometers, units, ppm, and others.
What constraints these characteristics impose on the deployment and upgrade phase
Multi-source heterogeneous data sources require adaptation to different protocols and data formats during deployment to avoid connection interruptions. The dual rhythm of high-frequency real-time updates and batch document imports requires balancing resource usage between stream processing and batch indexing during deployment. The massive volume of documents generated by ultra-long process chains requires sufficient storage and indexing capacity to accommodate large unstructured data such as wafer maps and defect maps. During the upgrade phase, compatibility with legacy heterogeneous data source links is required to avoid disrupting existing production environment document synchronization workflows due to version updates. Support for multi-architecture server deployment is also required to adapt to different hardware environments within wafer fabrication facilities. Configurations on this page are based on FastGPT v4.17.0.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://<username>:<password>@<host>:<port>/fastgpt?authSource=admin | Adapts to storage needs for multi-source heterogeneous data in wafer fabrication facilities, configures access credentials and authentication source for the corresponding database |
DB_MAX_LINK | 100–200 | Matches document volume generated per wafer batch and real-time update frequency, avoids exhausting database connections |
REDIS_URL | redis://:<password>@<host>:<port>/0 | Caches frequently accessed equipment recipes and yield report data to reduce load on the core database |
STORAGE_S3_ENDPOINT | http://<s3-host>:<port> | Stores large unstructured documents such as wafer maps and defect maps, adapts to batch upload requirements |
FE_DOMAIN | https://<your-fastgpt-domain> | Configures cross-domain access permissions, adapts to domain name rules for internal office networks in wafer fabrication facilities |
ROOT_KEY | Configured via actual testing | Configures administrator access keys to ensure access security for the deployment environment |
Three common mistakes
- Phenomenon: An error occurs when reading CSV-format batch run records after upgrading the version, prompting a field format mismatch. Cause: The legacy version used commas as the default CSV column separator, while the new version adapts to tab-separated process data commonly used in wafer fabrication facilities. Parsing fails because the configuration was not adjusted synchronously.
- Phenomenon: Containers fail to start after deployment on Arm64 architecture servers. Cause: The corresponding architecture image was not pulled. The default image only adapts to x86 architecture and cannot run in Arm64 environments.
- Phenomenon: Docker-deployed MongoDB cannot connect, prompting authentication failure. Cause: Custom credentials were not configured in the startup command. The default image credentials remain at their initial values and were not modified, resulting in access denial.
How to confirm the configuration is complete
- Run the
docker pscommand to confirm that all FastGPT-related containers are in a running state. - Upload a CSV file of wafer batch run records to verify that the chunking and indexing processes run without errors.
- Access the configured
FE_DOMAINto verify that the frontend interface loads normally and can call backend interfaces. - Check the storage bucket corresponding to
STORAGE_S3_ENDPOINTto confirm that uploaded documents such as defect maps and wafer maps have been successfully stored.
The values provided are common starting points and should be measured against the reader's own samples.