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[2602.21601] Deep Clustering based Boundary-Decoder Net for Inter and Intra Layer Stress Prediction of Heterogeneous Integrated IC Chip
Summary
This article presents a novel approach using a Deep Clustering based Boundary-Decoder Net for predicting inter and intra-layer stress in heterogeneous integrated circuits, demonstrating improved performance over existing methods.
Why It Matters
As the demand for advanced integrated circuits increases, understanding and predicting stress in heterogeneous materials becomes crucial for reliability and performance. This research contributes to the field of machine learning and computational engineering by proposing an innovative method that enhances stress prediction accuracy, potentially leading to better design and manufacturing processes in semiconductor technology.
Key Takeaways
- Introduces a Boundary-Decoder Net combined with deep clustering for stress prediction.
- Demonstrates superior performance in reducing training and testing errors compared to baseline methods.
- Utilizes a dataset of 1825 stress images for validation.
- Focuses on stress prediction in 3D heterogeneous IC packages subjected to thermal cycling.
- Highlights the importance of accurate stress modeling in semiconductor reliability.
Computer Science > Machine Learning arXiv:2602.21601 (cs) [Submitted on 25 Feb 2026] Title:Deep Clustering based Boundary-Decoder Net for Inter and Intra Layer Stress Prediction of Heterogeneous Integrated IC Chip Authors:Kart Leong Lim, Ji Lin View a PDF of the paper titled Deep Clustering based Boundary-Decoder Net for Inter and Intra Layer Stress Prediction of Heterogeneous Integrated IC Chip, by Kart Leong Lim and 1 other authors View PDF HTML (experimental) Abstract:High stress occurs when 3D heterogeneous IC packages are subjected to thermal cycling at extreme temperatures. Stress mainly occurs at the interface between different materials. We investigate stress image using latent space representation which is based on using deep generative model (DGM). However, most DGM approaches are unsupervised, meaning they resort to image pairing (input and output) to train DGM. Instead, we rely on a recent boundary-decoder (BD) net, which uses boundary condition and image pairing for stress modeling. The boundary net maps material parameters to the latent space co-shared by its image counterpart. Because such a setup is dimensionally wise ill-posed, we further couple BD net with deep clustering. To access the performance of our proposed method, we simulate an IC chip dataset comprising of 1825 stress images. We compare our new approach using variants of BD net as well as a baseline approach. We show that our approach is able to outperform all the comparison in terms of train an...