计量学
干涉测量
计算机科学
光学
堆栈(抽象数据类型)
电子工程
物理
工程类
程序设计语言
作者
Daniel Schmidt,Manasa Medikonda,Michael Rizzolo,Claire Silvestre,Julien Frougier,Andrew S. Greene,Mary Breton,Aron Cepler,Jacob Ofek,Itzik Kaplan,Roy Koret,Igor Turovets
出处
期刊:Journal of micro/nanopatterning, materials, and metrology
[SPIE - International Society for Optical Engineering]
日期:2023-02-21
卷期号:22 (03)
被引量:4
标识
DOI:10.1117/1.jmm.22.3.031203
摘要
A spectral interferometry technique called vertical travelling scatterometry (VTS) is introduced, demonstrated, and discussed. VTS utilizes unique information from spectral interferometry and enables solutions for applications that are infeasible with traditional scatterometry approaches. The technique allows for data filtering related to spectral information from buried layers, which can then be ignored in the optical model. Therefore, using VTS, selective analyses of the topmost part of an arbitrarily complex stack are possible within a single metrology step. This methodology helps to overcome geometrical complexities and allows for focusing on parameters of interest through dramatically simplified optical modeling. Such model simplifications are specifically desired for back-end-of-line applications. Three examples are monitored discussed: (i) the critical dimensions (CDs) of a first metal level on top of nanosheet gate-all-around transistor structures, (ii) the thickness of an interlayer dielectric above embedded memory in the active area, and (iii) the CDs of trenches on top of tall stacks in the micrometer range comprising many layered dielectrics. It was found that, in all three cases, data filtering through VTS allowed for a simple optical model capable of delivering parameters of interest. The validity and accuracy of the VTS solution results were confirmed by extensive reference metrology obtained by traditional scatterometry, scanning electron microscopy, and transmission electron microscopy. Furthermore, it was shown that machine learning models trained with VTS filtered data can converge to a robust solution with a smaller dataset compared with models training with traditional scatterometry data.
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