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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 lines. 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 scale 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 200 10-6m up to 4000 10-6m. 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 monitoring of the CD characteristic can be enabled through the use of machine learning methods. A classical monitoring scheme is typically based on measuring the deviation of each curve from the average behavior. However, the monitoring of a curve and deviations thereof often requires the evaluation of the overall shape of the curve. Thus we propose a monitoring concept which uses a support vector machine in order to learn the shapes of the CD characteristics. It is demonstrated that a statistical model of the CD characteristics can be trained and used in order to monitor single excursions (see Figure 1) as well as overall process changes.
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