What Data Looks Like for This Category
This category of data typically originates from wafer fab MES systems, equipment FDC modules, defect detection and metrology tools, wafer map repositories, process recipe libraries, and experimental datasets. Daily documentation includes process flow cards, batch run records, equipment recipes, SPC control charts, exception handling sheets, defect atlases, and yield reports. Data update cycles cover each wafer batch production run, real-time equipment parameter reporting, and daily summary statistics. Fields include wafer batch identifiers, nanoscale process parameters, defect spatial coordinates, equipment runtime duration, and additional relevant metrics. Units include nanometers, milliseconds, batch encoding formats, and other standard semiconductor manufacturing units.
Constraints Imposed on Database and Operations Workflows
Cross-table associative queries are required for multi-source heterogeneous process, equipment, and defect data to reconstruct the complete wafer production path. Nanoscale process parameters must use high-precision floating-point storage to prevent data distortion that could compromise defect attribution analysis. Real-time reported equipment data and batch records need to support high-concurrency writes and low-latency reads to meet fast traceability requirements. The unique identifier of a wafer batch must be strictly bound to the process path to ensure accurate positioning of associated nodes during exception traceability. Limited rework windows require the database to quickly filter associated equipment and process parameters for abnormal batches, avoiding ineffective operations.
How to Configure Settings
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt_wfr:SecurePass123@db-host:27017/fastgpt_wafers?authSource=admin | Supports storage of multi-source heterogeneous batch and defect document data for wafer manufacturing, and enables flexible unstructured queries |
PG_URL | postgresql://fastgpt_user:PassW@rd456@db-host:5432/wfr_trace?sslmode=require | Stores structured process recipe and SPC graphic data, and supports complex associative queries for process paths |
DB_MAX_LINK | 200–300 | Meets concurrent demand for real-time equipment data reporting and batch retrieval per wafer batch, and prevents task blocking caused by connection exhaustion |
REDIS_URL | redis://:RedisPass789@cache-host:6379/0 | Caches frequently accessed basic wafer batch information and thumbnail data of defect atlases, reducing load pressure on core databases |
Three Common Misconfigurations
- Bulk batch traceability tasks fail to respond, due to insufficient
DB_MAX_LINKconnection count configured, causing concurrent write requests to be blocked. - Authentication failure errors occur during database queries, due to failure to create an exclusive database user, and direct use of the root account for operations.
- Defect atlas query results are empty, due to failure to correctly bind wafer map data to batch identifiers, leading to failed matching of corresponding data during cross-table association.
How to Verify Successful Configuration
- Run the command
mongo "mongodb://fastgpt_wfr:SecurePass123@db-host:27017/fastgpt_wafers?authSource=admin"to verify that the exclusive user can normally read and write the wafer batch collection. - Run the command
psql "postgresql://fastgpt_user:PassW@rd456@db-host:5432/wfr_trace?sslmode=require"to query the process recipe table and confirm normal data read access. - Check Redis connection logs to confirm that the cache instance configured via
REDIS_URLcan establish normal connections and complete data caching. - Initiate a bulk batch traceability request, confirm that database connection counts do not trigger exhaustion alerts, and verify normal concurrent processing.
The values provided are common starting points and should be measured against the reader's own samples.