Insulated gate bipolar transistors (IGBTs) are the key component in power electronics, and the intricate relationship between their performance and structural parameters poses a formidable challenge in the design process. This article proposes an automatic optimal design method for IGBT structural parameters to leverage the pretrained machine learning (ML) model to efficiently predict the initial IGBT device’s performance, followed by utilizing the differential evolution (DE) algorithm to automatically adjust structural parameters based on the disparity between predicted and expected device performance until the expected performance is achieved. The method is validated in the design of punch-through IGBTs (PT-IGBTs) and trench gate field-stop IGBTs (FS-IGBTs), and the performance of technology computer-aided design (TCAD) simulation of the designed device is similar to the target performance. In particular, the simulation results of the designed FS-IGBT are highly fitted to the datasheet of the commercial device, which verifies the generalizability and effectiveness of the method. In addition, comparative analyses with various algorithms show DE provides the fastest optimization and extraordinary robustness under the exact specifications. Crucially, the proposed design scheme aligns with semiconductor physics. The method simplifies IGBT design without the need for manual tuning and TCAD tool simulation.

In this paper, we present a semantic-based partial retrieval approach of CAD models for design reuse. Based on the observation of reusable regions for design of 3D CAD models, for each model in the model library, we propose an algorithm that automatically extracts its reusable regions for partial retrieval. To further effectively support the partial retrieval of these reusable regions through simple queries, we represent each reusable region by all its local matching regions and describe each local matching region in a hierarchical way. Based on the hierarchical descriptor, a partial retrieval method for reusable regions is put forward. The approach proposed is implemented and tested by hundreds of mechanical parts. Preliminary results show that the method can effectively support partial retrieval for design reuse.