What this category of data looks like
The semiconductor packaging and testing process generates daily documents including packaging process cards, bonding and molding parameter tables, wafer incoming material maps, test program version files, yield reports, and failure analysis reports. Data sources include MES systems, bonding and molding equipment logs, ATE tester outputs, X-ray/SAM test results, and customer specifications. Data is updated once per production batch, once per chip test, and once daily. Document fields include batch number, process parameters, test bin code, yield values, and more. Units include custom formatted fields such as degrees Celsius, volts, and batch numbers.
What constraints do these characteristics impose on the document parsing and chunking link
This category has diverse document types, including structured tables, charts, and non-standard text. Data update frequency is high, requiring real-time synchronization of production data. Wafer incoming material maps have a strong correlation with batch numbers. Chunking must retain field context associations; otherwise, physical batches and test data cannot be aligned during ledger summary. Some documents contain embedded metadata, such as test program version numbers. Parsing must accurately extract complete content. Frequently updated production ledgers require parsing and chunking processes to have low latency, to avoid impacting real-time summary requirements.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the single-page text and table field density of packaging process cards and yield reports, avoids splitting parameter associations |
chunkSplitter | Semantic chunking + table anchoring | Addresses the large number of parameter tables and wafer map annotations in documents, preserves the integrity of structured content |
customPdfParse | Enabled | Supports parsing embedded wafer incoming material map metadata and test program version numbers, extracts custom fields in non-standard formats |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Adapts to the parsing time of large-size failure analysis reports and multi-batch yield reports, avoids timeout interruptions |
chunkSettingMode | Manual configuration | Adjusts chunking rules separately for different document types, adapts to differences between wafer incoming material maps and text-based documents |
Three common errors to avoid
- Parameter errors occur when parsing video-based production logs. The cause is failure to configure a parsing mode adapted to VLM models, which cannot recognize process parameter frames in video footage.
- Format errors are returned when calling the API to upload and parse images. The cause is failure to follow the multipart/form-data format requirements specified in the interface documentation, incorrectly using JSON format to transfer image binary data.
- Chunk results lose the batch number field corresponding to the wafer map. The cause is failure to retain context associations during chunking, splitting the batch number and wafer map into different chunks, leading to failure to align data during ledger summary.
How to confirm the configuration is properly set
- Upload a standard packaging process card PDF, review the parsed text content, confirm that parameters such as bonding temperature and molding pressure are fully retained and not split apart.
- Call the parsing API to upload a test program version file, check the returned chunk list, confirm that the test program version number is correctly extracted and included in the corresponding chunk.
- Upload a yield report larger than 100 MB, confirm that the parsing process does not trigger timeout errors, and all chunk content is fully generated.
- In the knowledge base management interface of FastGPT v4.17.0, view the chunk details of uploaded documents, confirm that the metadata of the wafer incoming material map is correctly associated with the corresponding batch field.
The values provided are common starting points and should be measured against the reader's own samples.