Modern semiconductor fabrication pushes the limits of chemistry and physics while simultaneously employing largescale, cutting-edge processing techniques. While fab expansion and capital expenditures continue to grow, the human element has become ever more demanding and prone to error. To assist with this issue, computer-aided process engineering, process control, and tool monitoring will continue to rise in the coming years. In this paper, we present an APC-integrated, customizable solution to an in-fab processing segment. Through machine learning, we combine information from design-specific extracted features with processing and metrology data to predict oxide deposition thickness. The result is a design-aware augmentation for current metrology that can recommend accurate process recipe conditions for new layouts. We also present experimental results highlighting the benefits of adding design-aware features with in-fab data to anchor and support each other across layouts and technologies. This result paves the way to decouple, isolate, and quantify the individual influences each processing step imposes on different designs at various stages of the fabrication flow.
Automotive semiconductor products demand high reliability. The current process of performing electrical test after fab-out may not be sufficient for efficient reliability management. This paper proposes an AI solution for improving the reliability of automotive semiconductor products. The solution includes two unique concepts: fab-data augmentation (FDA) to estimate missing values using partially available measurement data during the fabrication process and real-time prediction of reliability using machine learning (ML) models. The ML model is also used to identify and rank critical process steps that impact reliability, and to predict the reliability of wafers in real time. This allows low reliability wafers to be screened out early during the chip fabrication process, improving the overall reliability of the final product.
As processes become more complex, and more operations are needed to fabricate individual levels in a sem iconductor chip, the ability to leverage the wealth of information to fully monitor and control the process has become of critica l importance. In this work we extend the application of design-aware fabrication process models to more operations. While one could expect that the process models become more accurate with respect to the target of interest, one of the main benefits of applying this technique is that it further decouples the individual influences that every process step imposes to different designs at different stages of the fabrication process. These results have significant implications on how this methodology can be used to improve process monitoring, a nd in the future extend to process optimization and design specific process control.
KEYWORDS: Metrology, Bridges, Manufacturing, Data modeling, Time metrology, Semiconducting wafers, Reticles, Internet, Design for manufacturability, Data integration
In the world of today’s internet of things economy, the number of semiconductor designs is increasing rapidly. A cost effective way is needed to set up new designs for manufacturing. All available data sources need to be utilized to do the setup. In this paper we suggest two new approaches for reusing historical data for future designs: Combining historical fab-generated data with full reticle design features to predict optimal process conditions, and the concept of cross metrology integration of fab-generated data across multiple metrology steps to improve data quality.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.