Metrology in high-volume semiconductor
manufacturing is increasingly constrained by the need for
nanometer-level precision, extensive sampling, and the growing
complexity of device architectures. As the global demand
for semiconductors accelerates, there is an urgent need for
scalable solutions that enhance measurement efficiency and
throughput from manufacturing facilities without compromising
on measurement integrity. In this study, we present results
from an AI-driven in-line metrology framework developed
as part of this work, which leverages big data analytics and
machine-learning to significantly reduce measurement time
while maintaining accuracy, precision, and sensitivity. By
integrating AI with established film analysis algorithms, the
system performs spectral reconstruction to compensate for signal
degradation caused by reduced acquisition times. It is trained on
large-scale historical datasets, enabling reliable performance in
real-world production environments. Experimental evaluations
conducted in a high-volume manufacturing setting at a
GlobalFoundries facility demonstrate up to a twofold increase in
metrology throughput, while remaining within bounds to ensure
measurement integrity in terms of accuracy, precision, and
sensitivity. This advancement offers a scalable and cost-effective
approach to accelerate XPS metrology, with the potential to
support next-generation fabrication demands.
Keywords: XPS, Machine learning, In-line metrology, Semiconductor
manufacturing, High-volume manufacturing, Spectral
reconstruction, Throughput enhancement, Measurement accuracy,
Process control.