RepTor: An Intelligent Hybrid Neural Network Based Recipe Generator for Semiconductor Process Modeling and Characterization

An intelligent hybrid neural network based recipe generator is presented as a convenient tool for process optimization typically aiming highly-nonlinear plasma process in semiconductor manufacturing. As the wafer size continuously expanding up to 300 mm in current high-volume manufacturing (even forecasting 450 mm in 2012), fast and convenient process settlement cannot be over emphasized to meet the time-to-market of the newly developed products. In this paper, we suggest a recipe generator based on neural network and particle swamp optimization, nominally RepTor, to help minimizing material cost for process setup and maximizing accuracy of process modeling. RepTor is verified using SiO₂ deposition process for modeling and predicting the wafer geometry in conjunction with tool parameter. We have convinced the capability of the suggested recipe generator, and it provides a good starting point for further fine tuning of process optimization.