System-in-package design using multi-task memetic learning and optimization

System-in-Package (SiP) is an advanced packaging technology and developing rapidly in semiconductor industry. Electronic modules of this package type are individual integrated systems for specific applications. Therefore, those modules are usually characterized by multiple encapsulated components and sophisticated internal structures. However, such complexity brings great challenges to package design. Traditional methods, like design of experiments, response surface analysis, are widely used in this field, but their effectiveness drops rapidly due to increasing complexity. In current scenarios, not only do the amount of design variables increases, but also the modules have diverse design tasks to satisfy. Thereby, package design for SiP modules is a multi-task optimization problem. To resolve this issue, we propose a multi-task memetic learning and optimization algorithm, in which multi-output Gaussian process model and multifactorial evolutionary algorithm are employed. In this work, knowledge transfer between different tasks is activated during both the surrogate modeling and model optimization procedures. Several variants of the proposed algorithm are tested, and their modeling accuracy and optimization efficiency were compared. This interdisciplinary study shows the benefits of the memetic knowledge transfer mechanism in improving modeling and optimizing efficacy in multi-task scenarios and presents a viable approach to achieve both automation and optimization for complicated packaging design.