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Deploying Multivariate Analysis to Improve Fab Productivity

Gary Dagastine

Multivariate data analysis is a powerful statistical methodology made possible in recent years by the availability of increased computing power. The technique takes into account multiple variables simultaneously, enabling the study of complex data sets that are beyond the capabilities of traditional univariate analysis.

Multivariate data analysis shifts the focus from individual factors to relationships among variables, identifies root causes and indirect effects, and allows for the use of predefined models and data templates to speed the analysis.

Applied Materials is now offering proprietary multivariate data analysis as part of its FabVantage consulting solutions, which help customers increase overall fab efficiency and output. Using multivariate analysis, FabVantage consultants can identify root causes for factory-level issues such as capacity shortages, long cycle times, high wafer costs, line imbalances, high levels of work-in-process (WIP), and high variability, among others.


Applied’s FabVantage team begins each engagement with a top-down, data-driven assessment to identify issues that are having major impacts on fab productivity. A roadmap for improvement is created based upon comparisons of the customer’s data with benchmarks and best practices in the Applied Materials knowledge base. Probable causes for underlying performance gaps are then outlined, and a proposal is presented to the customer.

Part of the FabVantage Fab Productivity Practice toolkit for multivariate analysis is an internal fab analyzer built on the same platform as Applied’s APF Real-Time Dispatcher and Reporter—software tools that help manufacturers develop customized rules and improve dispatching decisions.

Applied’s fab analyzer contains many models built and tested based on Applied’s global experience, so consultants can move forward more quickly to help customers. An example using the fab analyzer is shown in figure 1.

Figure 1. The figure shows the results of a multivariate statistical analysis of the performance of a group of tools. It illustrates the ability of the technique to uncover the relationships among different variables that impact cycle time. The area highlighted in (a) shows that some tools with high uptime aren’t being used, even though (b) tells us that cycle time (i.e. “lot wait time”) is strongly driven by tool utilization. Also, (c) shows that among tools having low uptime, there is a high variability in uptime.

The company’s multivariate analysis tools not only help pinpoint the root causes of the problem, they also enable consultants to create a list of priorities so that FabVantage engineers can develop an implementation plan to quickly deliver major results to customers, often within a 3- to 6-month timeframe.


The FabVantage Consulting Group is frequently engaged to help customers find ways to squeeze more capacity out of their existing fabs by increasing throughput, which can eliminate or defer the need for sizeable capital investments.

“When we and the customer work together in a deep technical collaboration, we usually uncover cost-effective ways to increase overall fab capacity and throughput,” said Productivity Practice Manager Haim Albalak. “One very effective way to create this new capacity is through better management of the WIP flow.”

One example of this approach is to optimize product sequencing to reduce changeovers and increase throughput. In one instance, FabVantage consultants performed an analysis that examined the time distribution of repeating the same recipe within one of the customer’s ion implant tool groups. The analysis revealed a high frequency of repetition of the same recipe within a short window of time (less than two hours). This meant there was a potential opportunity to reduce setup time and improve tool output (see figure 2).

Figure 2. Analyzing recipe repetition within a 2-hour time window revealed an opportunity to reduce setup time and improve tool output by increasing the cascading level.

The next step was to find the optimal cascading level by analyzing the relationship between the cascade size and the lot cycle time. The optimal location is the point on the curve that represents the minimum lot cycle time (see figure 3). As a result of this analysis, the customer’s Real-Time Dispatching (RTD) rule was updated to support the new cascading target (see figure 4).

Figure 3. Finding the optimum cascading level to achieve minimum cycle time at one customer site. The figure shows the relationship between the cascade size and the lot cycle time. The optimal location is the point on the curve that represents the minimum lot cycle time.

Figure 4. Improved cascading level as a result of changing the lot dispatching rules.

Implementing the new dispatching rule improved the cascading level, reduced lot cycle time, and increased tool output by 5%.


Cycle time is another critical element of fab performance because it determines how quickly manufacturers can get products to market. In addition, variability in cycle time impacts a manufacturer’s ability to predict the timing of production, and therefore their ability to meet on-time delivery commitments. Tool dedication is a major factor that influences fab cycle time and throughput performance. In many cases the official tool qualification matrix that indicates which recipe is qualified to run on each tool does not reflect the actual dedication level. Often, the tool dedication is much higher. Analyzing fab historical data with the Applied fab analyzer allows FabVantage consultants to determine the real tool dedication level based on the actual number of tools that run each product step (see figure 5).

Figure 5. Tool dedication analysis using Applied’s fab analyzer.

Using multivariate analysis to evaluate the relationship in the fab between tool dedication and the lot waiting time (queue time) has revealed a strong correlation: when the lot has fewer tools capable of running it, the lot waiting time is higher (see figure 6).

Figure 6. FabVantage consultants used multivariate analysis to uncover a strong correlation between the number of tools that were actually dedicated to a particular recipe in a fab and resulting lot wait time (queue time).

In one instance, based on the analysis in figure 6, a roadmap to reduce factory cycle time by 20% was developed (shown schematically in figure 7). The primary strategy for decreasing the cycle time was to reduce the impact of tool dedications on WIP movement through the line.

Figure 7. Fab cycle time reduction roadmap based on improving tool dedication.

Minimizing the impact of tool dedication was accomplished in two ways. The first was to improve tool matching and increase the number of tools that are qualified to run the product steps with the highest wait times. The second was to modify the dispatching rules in RTD to reduce the probability that tools qualified to run fewer product steps would run out of lots they are capable of running.


Applied Materials has demonstrated that by deploying advanced multivariate analysis techniques, both the performance of Applied tools in the fab and the overall output of the fab itself can be improved.

Our modelling methods have helped customers improve scheduling, shift bottlenecks, reduce cycle time and cycle time variability, and increase the throughput of the fab. For many capacity-constrained customers, these analyses often have helped increase the output of their fabs with minimal capital investment.

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