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A sampling inspection using non-patterned wafer and photomask has been dedicated to classical inspection technology for monitoring trend of particle variation at mass production. Total cost of sampling inspection method decreases the overall equipment effectiveness in mass production because equipment usage time and raw material cost. Nevertheless, customer mass production yield requirements for high-grade photomask and extreme ultraviolet photomask by sampling inspection method will be difficult to satisfy. To overcome sampling inspection's low reliability, this paper intended to describe an application of real-time monitoring for mass production equipment and verification of evaluated case by case. Optimization of real-time monitoring setup requires that sensor's install location with a considered mean free path in vacuum chamber, avoid to particle and bubble in chemical tube line and filter, and digital image process comparing method for nozzle height and parts location. An emergence of many by-products in a vacuum chamber, chemical tube line, and chemical filter is an unexpected danger. Application of real-time monitoring contributes to observing particles that in vacuum chamber using plasma, in tube line using chemical and chemical filters, sensing of mechanical drift and twist are also applicable with real-time detection technology using high-resolution cameras. As mentioned above, saved real-time big data can use proactive control to improve yield loss and cost of ownership. The specific suggestion about using real-time monitoring method is as follows 1. Detected increase rapidly trend of particle. Stop a process and start a particle removing recipe. 2. Observed particle rising. Stop process and start a cure recipe, and change other best path. 3. Sensed abnormal action. Stop process and do preventive maintenance after all substrate out. Both real-time monitoring data and yield data can analyze correlations that improve to become low cost of ownership by figuring out a root cause and drop in quality. A photomask industry is small compare to semiconductor industries, less than 1 percent by number of tools and production capacity. A photomask industry hard to make a big data due to little seed or small volume data. This paper shows how to make big data using real-time monitoring technology and how to defend a yield loss by unexpected situation at photomask tools.
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