Advanced EUV Resist Characterization using Scatterometry and Machine Learning

Abstract

Comprehensive EUV resist characterization for line and space patterns at pitches between 32 and 40 nm using scatterometry in conjunction with machine learning algorithms is presented and discussed. Controlled experimental variations of EUV single expose resist lines were introduced by exposure dose and illumination conditions. Scanning electron microscopy and atomic force microscopy were used to collect reference data. The developed machine learning solutions allow for determination of four characteristic resist metrics with a single scatterometry measurement: line width, line height, line edge roughness, and line top roughness. Therefore, in excellent agreement with reference metrology, the scatterometry measurement, can replace four individual operations for rapid and accurate in-line monitoring.