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In the mask shop the challenges associated with today’s advanced technology nodes, both
technical and economic, are becoming increasingly difficult. The constant drive to continue
shrinking features means more masks per device, smaller manufacturing tolerances and more
complexity along the manufacturing line with respect to the number of manufacturing steps
required. Furthermore, the extremely competitive nature of the industry makes it critical for
mask shops to optimize asset utilization and processes in order to maximize their competitive
advantage and, in the end, profitability.
Full maximization of profitability in such a complex and technologically sophisticated
environment simply cannot be achieved without the use of smart automation. Smart
automation allows productivity to be maximized through better asset utilization and process
optimization. Reliability is improved through the minimization of manual interactions
leading to fewer human error contributions and a more efficient manufacturing line. In
addition to these improvements in productivity and reliability, extra value can be added
through the collection and cross-verification of data from multiple sources which provides
more information about our products and processes.
When it comes to handling mask defects, for instance, the process consists largely of time
consuming manual interactions that are error prone and often require quick decisions from
operators and engineers who are under pressure. The handling of defects itself is a multiple
step process consisting of several iterations of inspection, disposition, repair, review and
cleaning steps. Smaller manufacturing tolerances and features with higher complexity
contribute to a higher number of defects which must be handled as well as a higher level of
complexity.
In this paper the recent efforts undertaken by ZEISS to provide solutions which address these
challenges, particularly those associated with defectivity, will be presented. From automation
of aerial image analysis to the use of data driven decision making to predict and propose the
optimized back end of line process flow, productivity and reliability improvements are
targeted by smart automation. Additionally the generation of the ideal aerial image from the
design and several repair enhancement features offer additional capabilities to improve the
efficiency and yield associated with defect handling.
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