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Virtual manufacturing that is enabled by rapid, accurate, full-chip simulation is a main pillar in achieving successful
mask tape-out in the cutting-edge low-k1 lithography. It facilitates detecting printing failures before a costly and time-consuming
mask tape-out and wafer print occur. The OPC verification step role is critical at the early production phases
of a new process development, since various layout patterns will be suspected that they might to fail or cause
performance degradation, and in turn need to be accurately flagged to be fed back to the OPC Engineer for further
learning and enhancing in the OPC recipe. At the advanced phases of the process development, there is much less
probability of detecting failures but still the OPC Verification step act as the last-line-of-defense for the whole RET
implemented work.
In recent publication the optimum approach of responding to these detected failures was addressed, and a solution was
proposed to repair these defects in an automated methodology and fully integrated and compatible with the main
RET/OPC flow. In this paper the authors will present further work and optimizations of this Repair flow.
An automated analysis methodology for root causes of the defects and classification of them to cover all possible causes
will be discussed. This automated analysis approach will include all the learning experience of the previously
highlighted causes and include any new discoveries. Next, according to the automated pre-classification of the defects,
application of the appropriate approach of OPC repair (i.e. OPC knob) on each classified defect location can be easily
selected, instead of applying all approaches on all locations. This will help in cutting down the runtime of the OPC repair
processing and reduce the needed number of iterations to reach the status of zero defects. An output report for existing
causes of defects and how the tool handled them will be generated. The report will with help further learning and
facilitate the enhancement of the main OPC recipe. Accordingly, the main OPC recipe can be more robust, converging
faster and probably in a fewer number of iterations. This knowledge feedback loop is one of the fruitful benefits of the
Automatic OPC Repair flow.
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