Reinforcement learning framework for the mechanical design of ASIC and Interposer under multiphysics constraints

This study develops reinforcement learning based techniques for the design of microelectronic components under multiphysics constraints.

While traditional design approaches based on global optimization approaches are effective when dealing with a small number of design parameters, as the complexity of the solution space and of the constraints increases different techniques are needed.
This is an important reason that makes the design and optimization of microelectronic components (characterized by large solution space and multiphysics constraints) very challenging for traditional methods.
By taking as prototypical elements an application-specific integrated circuit (ASIC) and a heterogeneously integrated (HI) interposer, we develop and numerically test an optimization framework based on reinforcement learning (RL).
More specifically, we consider the optimization of the bonded interconnect geometry for an ASIC chip as well as the placement of components on a HI interposer while satisfying thermoelastic and design constraints. This placement problem is particularly interesting because it features a high-dimensional solution space.

Code implementation:

ASIC Design: FCNN_ASICTempStress_pred.ipynb - FCNN based surrogate model for max stress and temp evaluation, Q-learning_ASICDesign_TandS.ipynb - RL model to design optimal solder bump configuration in ASIC.
HI interposer Design: FCNN_InterposerTempStress_pred.ipynb - FCNN based surrogate model for max stress and temp evaluation, PPO_torch_TandS.ipynb - RL model to design optimal placement of components on the HI interposer.