Advanced process control by machine learning-based virtual metrology for high product mix manufacturing

Abstract

The semiconductor foundry industry struggles with challenges in high-product-mix manufacturing, necessitating enhanced flexibility to address diverse customer demands. Coordinating multiple chambers and process steps with varying designs and technology nodes poses complexities leading to reduced yields and increased costs. The chemical vapor deposition (CVD) process introduces thickness variations due to device layout design and chamber condition drift, impacting transistor parameters and yield. Managing chamber-by-chamber variations is critical for high-volume manufacturing, yet traditional solutions fall short in fab line management, resulting in throughput loss. Conventional VM relies on data from the process chamber, referred to as fault detection and classification (FDC), to predict metrology results. In this study, the extraction of design features for better predictive purposes across diverse layouts and technologies are highlighted. Siemens' Calibre® software is employed for design feature extraction, and machine learning (ML) methodologies to construct the extended VM model. An advanced process control (APC) system using the extended VM model for run-to-run (R2R) control is proposed. It incorporates design features, FDC, and measurements to achieve the desired thickness target. The system triggers updates to the extended VM model based on prediction errors. Through control simulations, the APC system significantly enhances process capability and reduces film thickness variations, confirming its effectiveness in a high-mix product foundry fab. Responding to the growing demand for custom-designed products, this paper suggests integrating an ML-based extended VM model with design features and FDC into the APC system, presenting a promise solution for high-product-mix manufacturing.