An AI pipeline combining document parsing, curve digitization, and an iterative Bayesian optimizer converts PDF datasheets into ASM-HEMT SPICE models automatically, with reported fitting errors of 1.2 to 4.9 percent on 17 commercial HEMT devices from 10 manufacturers.
EDocNet: Efficient Datasheet Layout Analysis Based on Focus and Global Knowledge Distillation
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abstract
When designing circuits, engineers obtain the information of electronic devices by browsing a large number of documents, which is low efficiency and heavy workload. The use of artificial intelligence technology to automatically parse documents can greatly improve the efficiency of engineers. However, the current document layout analysis model is aimed at various types of documents and is not suitable for electronic device documents. This paper proposes to use EDocNet to realize the document layout analysis function for document analysis, and use the electronic device document data set created by myself for training. The training method adopts the focus and global knowledge distillation method, and a model suitable for electronic device documents is obtained, which can divide the contents of electronic device documents into 21 categories. It has better average accuracy and average recall rate. It also greatly improves the speed of model checking.
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Automated HEMT Model Construction from Datasheets via Multi-Modal Intelligence and Prior-Knowledge-Free Optimization
An AI pipeline combining document parsing, curve digitization, and an iterative Bayesian optimizer converts PDF datasheets into ASM-HEMT SPICE models automatically, with reported fitting errors of 1.2 to 4.9 percent on 17 commercial HEMT devices from 10 manufacturers.