Strategies on quantitative data preparation for OPC model calibration to reduce catastrophic failure at 7nm node

Abstract

The paper aims to propose a systematic methodology and the relevant strategies during data preparation to reduce catastrophic failures at 7nm or smaller technology nodes when calibrating optical proximity correction (OPC) models. The common loop and the work flow to build up an OPC empirical model is reviewed first. The strategies to discuss are focusing on the steps of data collection and data preparation. Pattern sampling selection is not a part of the discussions in this paper. The primary metrology technology to accomplish data collection for OPC model calibration is Critical Dimension (CD) Scanning Electron Microscope (SEM). Collected data set is reported as a combination of CD values at various heights of side wall of the measured features and the corresponding top-view images. A challenging mission at the data preparation step is to establish a systemtic methodology with a set of reliable filtering criteria and an algorithm that can perform the data qualification check effectively and automatically. The major concepts and terms proposed from the criteria are based on data analysis on the achievable information from CD SEM and the validity of the methodology is proven from mathematical perspective as well, which is targeted on obtaining additional insights of patterns before a standard or customized OPC model creation. The paper is summarized with conclusions.