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.

翻译：当三维异构集成电路封装在极端温度下经历热循环时，会产生高应力。应力主要出现在不同材料之间的界面处。我们利用基于深度生成模型的潜在空间表示来研究应力图像。然而，大多数深度生成模型方法是无监督的，这意味着它们依赖于图像配对（输入与输出）来训练模型。相反，我们采用了一种近期的边界解码器网络，该网络利用边界条件和图像配对进行应力建模。边界网络将材料参数映射到由其对应图像共享的潜在空间。由于这种设置在维度上是不适定的，我们进一步将边界解码器网络与深度聚类相结合。为了评估所提方法的性能，我们模拟了一个包含1825张应力图像的集成电路芯片数据集。我们将新方法与边界解码器网络的多种变体以及基线方法进行了比较。结果表明，我们的方法在训练和测试误差降低方面均优于所有对比方法。