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High productivity is a key requirement for today's advanced lithography exposure tools. Achieving targets for
wafers per day output requires consistently high throughput and availability. One of the keys to high availability
is minimizing unscheduled downtime of the litho cell, including the scanner, track and light source. From the
earliest eximer laser light sources, Cymer has collected extensive performance data during operation of the
source, and this data has been used to identify the root causes of downtime and failures on the system. Recently,
new techniques have been developed for more extensive analysis of this data to characterize the onset of typical
end-of-life behavior of components within the light source and allow greater predictive capability for identifying
both the type of upcoming service that will be required and when it will be required.
The new techniques described in this paper are based on two core elements of Cymer's light source data
management architecture. The first is enhanced performance logging features added to newer-generation light
source software that captures detailed performance data; and the second is Cymer OnLine (COL) which
facilitates collection and transmission of light source data. Extensive analysis of the performance data collected
using this architecture has demonstrated that many light source issues exhibit recognizable patterns in their
symptoms. These patterns are amenable to automated identification using a Cymer-developed model-based fault
detection system, thereby alleviating the need for detailed manual review of all light source performance
information. Automated recognition of these patterns also augments our ability to predict the performance
trending of light sources.
Such automated analysis provides several efficiency improvements for light source troubleshooting by providing
more content-rich standardized summaries of light source performance, along with reduced time-to-identification
for previously classified faults. Automation provides the ability to generate metrics based on a single light source,
or multiple light sources. However, perhaps the most significant advantage is that these recognized patterns are
often correlated to known root cause, where known corrective actions can be implemented, and this can therefore
minimize the time that the light source needs to be offline for maintenance. In this paper, we will show examples
of how this new tool and methodology, through an increased level of automation in analysis, is able to reduce
fault identification time, reduce time for root cause determination for previously experienced issues, and enhance
our light source performance predictability.
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