On the road to automated production workflows in the back end of line

Gilles Tabbone; Kokila Egodage; Kristian Schulz; Anthony Garetto

doi:10.1117/12.2326908

19 September 2018 On the road to automated production workflows in the back end of line

Gilles Tabbone, Kokila Egodage, Kristian Schulz, Anthony Garetto

Proceedings Volume 10775, 34th European Mask and Lithography Conference; 107750N (2018) https://doi.org/10.1117/12.2326908
Event: 34th European Mask and Lithography Conference, 2018, Grenoble, France

Abstract

The technical roadmap adopted by the semiconductor industry drives mask shops to embrace advanced solutions to overcome challenges inherent to smaller technology nodes while increasing reliability and turnaround time (TAT). It is observed that the TAT is increasing at a rapid rate for each new ground rule. At the same time, productivity and quality must be ensured to deliver the perfect mask to the customer. These challenges require optimization of overall manufacturing flows and individual steps, which can be addressed and improved via smart automation. Ideally, remote monitoring, controlling and adjusting key aspects of the production would improve labor efficiency and enhance productivity. It would require collecting and analyzing all available process data to facilitate or even automate decision-making steps. In mask shops, numerous areas of the back end of line (BEOL) workflow have room for improvement in regards to defect disposition, reducing human errors, standardizing recipe generation, data analysis and accessibility to useful and centralized information to support certain approaches such as repair. Adapting these aspects allows mask manufacturers to control and even predict the TAT that would lead to an optimized process of record.

Citation Download Citation

Gilles Tabbone, Kokila Egodage, Kristian Schulz, and Anthony Garetto "On the road to automated production workflows in the back end of line", Proc. SPIE 10775, 34th European Mask and Lithography Conference, 107750N (19 September 2018); https://doi.org/10.1117/12.2326908

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