What this category of data looks like
Data for this category comes from wafer test maps, MES systems, bonding and molding equipment, ATE test machines, X-ray/SAM inspection equipment, and customer specification documents. Daily maintenance documents include packaging process cards, bonding and molding process parameters, wafer incoming material maps, test program versions, yield reports, and failure analysis reports. Data is updated per wafer batch, per individual chip test result, and daily equipment operation logs. Document fields include process parameter thresholds, test bin codes, failure location identifiers, and other relevant metadata. Units cover voltage, time, packaging dimensions, and other industry standard units.
What constraints do these characteristics impose on model access and configuration
This category of data includes frequently updated real-time test data and static process documents. Model access must support synchronized reading of multi-source heterogeneous data. Equipment operation logs and test results are updated daily, so interfaces that support short-cycle data pulling must be configured. Wafer incoming material maps are bound to test program versions by batch, so the model must associate batch identifiers to align data. Parameter units vary across different equipment, so unit standardization processing must be completed before model input to prevent the model from confusing process data of different dimensions. Failure analysis reports include unstructured text and image data, so model access items that support multimodal input must be configured to meet the recognition needs of image-based failures.
How to set the configuration
This configuration follows the FastGPT v4.17.0 version specification.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long context requirements of multiple documents such as packaging process cards and failure analysis reports, avoiding truncation of critical process parameters |
temperature | 0.2–0.4 | Equipment inspection and maintenance require stable process parameter references; a lower temperature ensures output consistency |
| Workflow/API tool selection | auto | Automatically selects tools to process equipment logs and test data, adapting to the multi-source data Q&A scenario |
vision | Enabled | Adapts to multimodal Q&A requirements for wafer incoming material maps and X-ray inspection images, meeting the image recognition needs of failure analysis |
maxTokens | 1500–2000 token | Controls the output length of a single round of Q&A, preventing interruptions caused by overly long descriptions of equipment maintenance steps |
AIPROXY_API_TOKEN | Exclusive key obtained from the deployment platform | Ensures the security of interface calls, adapting to the access permission requirements of multi-source data |
Three common mistakes
- Phenomenon: When the model calls the equipment log query tool, the output is interrupted midway, and only partial maintenance steps are returned. Cause: The
maxTokensparameter is not configured correctly, and the output length exceeds the preset threshold and is forcibly truncated. - Phenomenon: No text conversion result is returned after uploading an audio file. Cause: The model access item that supports voice input is not configured, or the tool chain for audio analysis is not enabled.
- Phenomenon: The result returned when calling a local large model is inconsistent with that of an online API call. Cause: The value range of parameters such as
temperatureis not adjusted for the locally deployed model, and the behavioral differences between local and cloud models are confused.
How to confirm the configuration is correct
- Run a Q&A test for packaging process cards, check whether the returned content includes complete bonding and molding parameters, and confirm that the
maxContextconfiguration does not truncate critical documents. - Upload a wafer incoming material map and ask about the failure location, check whether the model can associate image and text data, and confirm that the
visionconfiguration is effective. - Initiate a multi-round Q&A for equipment maintenance, observe whether the output is complete, and confirm that the values of
maxTokensandtemperaturemeet the scenario requirements. - Call the test interface, check whether the returned log data comes from the correct MES or equipment data source, and confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are correct.
The values provided are common starting points and should be measured against the reader's own samples.