Artificial Intelligence Reshaping the Semiconductor Metrology
Abstract
As technology advances, the challenges in characterization of nanoelectronics devices and materials also increase. To overcome the challenges, relying on only cutting-edge hardware doesn’t help the metrologist in this new generation. Computational power of machine learning (ML), hence artificial intelligence (AI) has come forward as a strong support to modern metrology tools and data analysis. Here we review how AI is reshaping the metrology power for the nanoelectronics manufacturing community, especially in failure analysis and materials characterization. In this chapter, diverse contributions of AI in major metrology tools such as experiment automation, sample identification, data denoising, fast and robust data analysis etc. are discussed with their impact on the characterization and advancement of nanoelectronics.
Fields of Study & Subjects
Machine Learning in Materials Science
Integrated Circuits and Semiconductor Failure Analysis
Advanced Electron Microscopy Techniques and Applications
Publication details
- Journal / source
- Machine intelligence for materials science
- Publisher
- Springer Nature Switzerland
- Pages
- 189–215
- Publication date
- 1 January 2026
- Language
- EN
- Document type
- Book chapter