This study focuses on the development of 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. Ce
placement problem is particularly interesting because it features a
high-dimensional solution space.

Cet article explore les excursions dans le temps et leurs implications.

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