Semiconductor manufacturing requires that many very different processes succeed with a high degree of precision, including material handling and preparation, chemical processing, mask preparation, and photo lithography. The large number of steps and extraordinary tolerances make it difficult to adequately control tolerances and defects. For example, thermal management issues at a single stage can result in failed chips. Applying traditional methods to investigate such issues can require substantial and often infeasible simulation time or data collection.
The solution is to use artificial intelligence and machine learning to first train accurate predictive models using data of the process or related simulations. Once trained, such models eliminate the need for further data collection. With the roadblock of computational cost removed many otherwise infeasible analyses may be conducted to help identify the sources of manufacturing uncertainty and improve the overall process.
Join us for this webinar to learn how machine learning models trained on recorded data or simulations can be used to:
