Abstract
The inverse design approach would be advantageous when applied to ultra-thin packages because we can design the packages for an acceptable warpage profile at the start of the design process, instead of only being able to measure the warpage after the parts are built, as is the norm with a conventional design process. A framework for the inverse design of ultra-thin electronic packages is proposed in this work. We start with the design (desired / acceptable) warpage profile and move through the framework to determine the optimum metal densities at different substrate subsections and layers that would ultimately result in the design warpage profile. The framework consists of three main phases - learning the material properties of the substrate, establishing a link between substrate design parameters and warpage and finally carrying out inverse design using a global optimization algorithm. This study utilizes a unique cocktail of machine learning techniques and algorithms to achieve this inverse design goal. Results indicate that the framework can recommend changes to the metal density distribution across the substrate in order to bring about a 20% reduction in warpage.