基于深度学习的光调制光学反射离子注入参数表征

Xuesong Wang, Lijun Zhang, Yong Sun, Jing Min, Zhongyu Wang, Shiyuan Liu, Xiuguo Chen, Zirong Tang
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引用次数: 0

摘要

光调制光学反射(PMOR)是测量集成电路制造中晶体管掺杂分布的一种理想的超浅结面积计量技术,而植入能量、植入角度和植入剂量等工艺参数的表征对离子注入工艺的准确性有重要影响。本研究利用深度学习并发分析各种工艺参数,并评估了其在掺硼硅样品上的性能,数据来自 PMOR 中载流子照明(CI)实验测量的功率曲线,开发并训练了具有多任务学习架构的深度学习模型,以表征多个工艺参数,针对工艺参数采用多任务学习架构的 PMOR 模型在表征的准确性和速度方面都表现出卓越的性能。分析表明,将深度学习方法应用于 PMOR 技术中的 CI 计量是可行的。特别是,与传统的载流子辐照技术相比,在获得植入剂量和掺杂曲线的同时,还能获得植入能量、植入角度和植入类型等其他工艺参数,更有助于以可接受的精度和时间成本准确实现离子注入工艺。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning-based characterization of ion implantation parameters for photo modulated optical reflectance
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.
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