Deep learning-based characterization of ion implantation parameters for photo modulated optical reflectance

Abstract

Photo modulated optical reflectance (PMOR) is an ideal ultra-shallow junction area metrology technique for measurement of transistor dopant distribution in integrated circuit fabrication, and the characterization of process parameters such as implant energy, implant angle, and implant dose has a significant impact on the accuracy of the ion implantation process. This study utilized deep learning to analyze various process parameters concurrently and assessed its performance on boron-doped silicon samples, the data were obtained from the power curves measured from Carrier Illumination (CI) experiments in PMOR, a deep learning model with multi-task learning architecture was developed and trained to characterize multiple process parameters, and the PMOR model incorporating a multi-task learning architecture for process parameters demonstrated superior performance in terms of accuracy and speed of characterization. The analyses indicated that applying deep learning methods to CI metrology in PMOR technology is feasible. In particular, compared with the conventional carrier irradiation technique, the ability to obtain the implantation dose and doping profile along with other process parameters such as implantation energy, implantation angle, and implantation type can better assist in the accurate realization of the ion implantation process with acceptable accuracy and time cost.