Deep Clustering based Boundary-Decoder Net for Inter and Intra Layer Stress Prediction of Heterogeneous Integrated IC Chip

TL;DR

This paper introduces a novel deep clustering boundary-decoder network for predicting inter and intra-layer stress in heterogeneous IC chips, outperforming existing methods by leveraging boundary conditions and clustering.

Contribution

The paper proposes a new boundary-decoder network combined with deep clustering for stress prediction in IC chips, addressing the ill-posed nature of the problem.

Findings

Outperforms baseline methods in train and test error.

Uses simulated stress image dataset of 1825 samples.

Effectively models stress distribution at material interfaces.

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 and test error reduction.