Abstract
Package design process has evolved from the early days, when any design was subjected to vigorous analysis using Design of Experiments ( DOE) methodology to understand the effect of each parameter and material change, to the introduction of finite element analysis (FEA) to reduce the DOE runs or completely eliminate it in some cases. FEA has proved to be a valuable tool. With the easy accessibility to computing power and the recent advances in AI-algorithms, this paper presents the next evolution in package design - AI-assisted package design. This framework incorporates both physics-driven and data-driven approaches to develop accurate and efficient design solutions. A validated AI-assisted package design framework is presented here to improve the design of the distribution of metal lines and layers in the substrate with the objective of reducing warpage. The framework consists of three phases - assembling the dataset, building a surrogate model to link substrate design to warpage and finally using global optimization routines and the surrogate model to propose changes to the metal layer density distribution across the substrate so as to reduce warpage.