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The currently increasing demand for photo-masks in the regime of the 14nm technology drives many initiatives towards capacity and throughput increase of existing production line. Such improvements are facilitated by improved control mechanisms of the tools and processes used within a production line. While process control of long range parameters such as the average CD behavior is demanding yet conceptually well understood, other parameters such as the small scales CD properties are quite often elusive to process control. These properties often require a dedicated test mask to be processed in order to be validated. In this paper we introduce a systematic approach towards a product based monitoring of small scale CD behavior which uses a CD characteristic extracted from the defect inspection process. This characteristic represents the influence of CD relevant processes starting from 200m up to 4000 m. Large variations in the scale and magnitude of the CD characteristic are induced by layout specific design variations. However, the shape of these distinct curves is remarkably similar, which enables their use for monitoring as well as controlling the mask processes on the above stated spatial scales. In this paper it is demonstrated, that a meaningful process evaluation can be performed by using the classification capabilities of the support vector machines. The small scales CD characteristics presented in figure 1 originate from two distinct tools. Matching of the two tools can be assessed by training a support vector machine to classify the small scales CD characteristics according to their origin. The classification performance on the resampled training set as well as on the validation set is a robust measure for tool matching. The results of this approach are depicted in figure 2. The left panel shows the AUC statistics of bootstrapping resamples for tool comparison “A”. In this case no noticeable difference between the two tools is found (an average AUC of 0.55 suggest no learnable difference). This is contrasted by the tool comparison “B”, here the classifier has an average AUC of 0.75, indicating a learnable difference in the tool performances. This result is backed by the process understand of both tool types.
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