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In this paper, we present techniques that can be used to answer the following two questions: 1) how many wafers need to be allocated per treatment to detect a given difference in a device performance metric and 2) how can one determine if a given treatment significantly improved a performance metric? The approach presented here does not make any assumptions regarding the shape of the distribution or the spatial dependency structure for the within-wafer performance measurements and remains applicable for a variety of performance metrics, such as mean, variance, and median. The analysis method can be used in decisions regarding the appropriateness of allocating half or quarter wafer splits to a treatment. Furthermore, the approach allows us to evaluate and compare within-wafer sampling strategies for comparing performance metrics from competing flows. Index Terms - Metrology, nonparametrics, power curves, process development, sample size, test of significance, Type I and II errors.

Process technology development constitutes a significant cost in manufacturing integrated circuits. In this paper, we present a model-based approach for developing new process technology rapidly and inexpensively, using the salicide process to demonstrate the concepts. This approach is applied to evaluate performance tradeoffs, to develop insight into the underlying process physics, to quantify the impact of the salicide process on the device and circuit performance, and to estimate the process variability. The key idea of this approach is to group a sequence of process steps into a process module, and build simple and accurate process models for the module. The paper also illustrates the use of this model-based approach in synthesizing optimal processes rapidly based on requirements, contributing to the reduction of technology development cost and cycle time.

This paper presents a process monitoring system, which is designed to be used for monitoring VLSIC and other multistage manufacturing processes. The proposed process monitor can 1) simultaneously detect a variety of out-of-control conditions, 2) quantify the magnitude of process change, and 3) be used to compute the probability of meeting specifications. Average run length simulations show that for a single-stage process, the monitor is at least as good as the Shewhart-CUSUM charts for detecting changes in the distribution of the monitored characteristics. For a multistage process, however, the Bayesian monitor can significantly reduce the detection time by using in-line correlation information from earlier stages. The monitor has been applied to data from a state-of-the-art fabrication facility, and the results are promising.

Due to global warming and the consequent rise in ocean water temperatures, fish are being forced to migrate northward in search of cooler waters. Since the livelihood of fishing companies is dependent on proximity to fish, this migration poses a problem for small fishing companies that lack the resources to reach migrating fish. Particularly worrisome is the migration of Atlantic herring and Atlantic mackerel, the two most valuable species in the Scottish fishing industry. We compute a linear regression equation for each geographic coordinate in the Scotland region to predict temperature over time, based on historical sea-surface temperature data. Overall, we find that the Scotland region ocean temperature increases an average of 1.6!C over the next 50 years. To predict fish migration patterns, we develop an agent-based model with schools of fish moving stochastically on a dynamic field of ocean temperatures that change according to the linear regression equations. We generate equations that form the basis for each fish's decision-making behavior on a micro-scale. This model takes into account a wide variety of relevant factors and parameters, including total population, initial spatial density distributions, optimal temperature ranges for each species, number of fishing vessels, vessel capacity, fishing area radius, catch proportion, etc., all based on information from reputable sources. Our scalable model allows for visualization of migration patterns over time. Overall, herring and mackerel migrate northwest toward Iceland, though herring migrate much more rapidly due to their stricter preference for colder temperatures. To assess the impact of fish migration on small fishing companies, we evaluate the viability of different fishery locations by recording the time until fish migrate out of range. Additionally, to test the sensitivity of our model, we use the statistical variation in the sea-surface temperature data to estimate the most-likely, best-, and worst-case scenarios. Herring are projected to leave the coast of Scotland by 2051 (2038 in the worst case, 2056 in the best case); with their higher temperature tolerance, the earliest that mackerel are projected to migrate out of reach is in the early 2060s. By utilizing the temperature-dependent migratory patterns of simulated fish, we can project total revenue for each of the fishing companies along Scotland's coastline. Herring are expected to return a maximum of $42 million per fishery, while mackerel can return up to $204 million in the Shetland Islands. Although mackerel are more profitable in the long run than herring, the revenue for both species substantially decreases, both over time and for more-southern locations. To recoup the loss in revenue, we propose and test three solutions: relocating fishing ports, harvesting haddock (a different species), and upgrading vessel assets. Out of the 87 possible coastal locations, the Shetland Islands are the best possible location, with revenue generally decreasing moving southward. Harvesting haddock would prove to be more consistently profitable; their current habitat is south of Scotland, allowing them to migrate into Scotland waters over time. Finally, upgrading fishing vessels to include on-board refrigeration would increase revenue by around 43% in the north to over 1700% in the south. We create an effective and robust model to predict the patterns of fish migration over time due to climate change and their impact on small fishing companies on Scotland's coast. Additionally, our analysis of the impacts of parameter variation and possible solutions for fishing companies allows fishing companies to understand and respond to the severity of the oncoming crisis. [ABSTRACT FROM AUTHOR]

Run-to-run and supervisory control algorithms determine the equipment recipe to produce a desired output wafer state given the incoming wafer state and the current equipment model. For simple, low-dimensional equipment models, this problem is not difficult. However, when there are multiple responses for the system and the equipment models are nonlinear, automated synthesis of recipes is complicated by the potential for multiple solutions. While there are standard techniques for handling such inverse problems in general, each of these techniques is optimal only under certain conditions. We present a framework for performing automated synthesis of recipes that integrates database search, local optimization, and global optimization into a consistent methodology that is applicable to a wide range of equipment models and inversion problems in general. The integrated framework imposes quasi-continuity on the extracted recipes, is scalable to systems of high dimensionality, and can be optimized to minimize the expected synthesis time for any given problem. The framework has been implemented in a system that performs statistical optimization of CMOS transistor designs. The integrated framework provides a factor of 16 increase in performance over global optimization and a factor of three increase over exhaustive search and multiple starts of a local optimizer.