Modeling the Dependencies between Circuit and Technology Parameters for Sensitivity Analysis using Machine Learning Techniques

The sensitivity of integrated circuit parameters regarding manufacturing process variation represents a very important ongoing topic in the semiconductor industry. Establishing the functional relationship between them at an early stage, i.e. simulation, would create an advantage in terms of circuit improvement and eventually high production yield. This paper presents a methodology for finding the influence of technology parameters (i.e. Process Control Monitor parameters) on device performance. The methodology is based on Machine Learning algorithms and Bayesian Optimization framework with the purpose of modelling the functional dependencies between technology and circuit parameters. The experimental results prove that the device performance is highly sensitive to technology parameters variation and this dependency can be modelled.