Novel ellipsometry metrology-based machine learning technique for low sensitivity characterization of critical dimensions within gate-all-around transistors

Abstract

The semiconductor industry has witnessed a fast progression of spectroscopic ellipsometry (SE) techniques aimed at resolving a plethora of complex device characterizations on a nanometric scale. The Mueller Matrix (MM) methodology coupled with rigorous coupled-wave analysis (RCWA) has offered an unprecedented power of investigation and analysis of diverse critical dimensions (CDs), especially when applied to gate-all-around (GAA) structures, as it helps increase the useful spectral signals of the often geometrically buried CDs. However, the sensitivity to the CDs can be often screened by other parameters, hampering the precision and accuracy of the measurement. Combining the most sensitive MM elements has therefore become a critical step of scatterometry critical dimension (SCD) metrology. Driven by the rapid developments of Machine Learning (ML) algorithms, we propose a versatile ellipsometry methodology that overcomes poor sensitivity and increases accuracy through a novel principal component analysis (PCA) method of the ML training algorithm with RCWA assistance. Furthermore, our methodology introduces a new ML training concept based on reference data statistics, rather than raw reference. Our approach has been validated with reference data and proved successful in monitoring GAA sheet-specific indent. The proposed methodology paves the way to measuring low sensitivity CDs with highly accurate, noise-reduced and robust ML-based physical SCD models for any logic and memory application.