- A revolutionary atom-thin semiconductor for electronics
- Panasonic’s photovoltaic module HITTM adopted for Toyota Prius PHV
- Former Santos CEO joins Redflow board
- Keysight Technologies’ InfiniiVision oscilloscopes make debut
New techniques can help companies make better decisions by using accurate, reliable, and scientific information to analyze risk, optimize process, and predict failure.
When used effectively, advanced analytics can not only significantly improve operations and margins but also spur growth. Yet many companies, including several semiconductor players, have been slow to embrace these techniques.
According to the International Data Corporation, the global pool of data is more than 2.8 zettabytes and growing, but companies generally use only about 0.5 per cent of that ocean of information to make decisions.
Businesses -usually consumer-facing ones -that do collect and analyze a broad range of data achieve many benefits. Banks, insurers, and retailers, for example, have used insights from advanced analytics to build sustained competitive advantages, including stronger customer relationships and greater operational efficiency.
Semiconductor companies have been leaders in generating and analyzing data. But few have effectively applied advanced analytics to fab operations, where they could improve predictive maintenance and yield, or to R&D and sales-force effectiveness, cross-selling, portfolio optimization, and other tasks.
But we may soon see the more widespread adoption of advanced analytics in semiconductors. First, computing power and storage infrastructure have become markedly easier to deploy with the advent of cloud computing. Second, there has been a step change in the power of the tools used to extract, aggregate, manage, analyze, and display data, as well as in techniques to incorporate data into actionable models. Hadoop applications, for example, have made it possible to extract insights from unstructured data by simplifying the integration of disparate data sources.
Finally, slower industry growth is prompting semiconductor companies to look for operational efficiencies, including some that may be more easily identified and deployed with advanced analytics.
Advanced analytics can provide a framework to unlock insights about where to invest and how to improve the performance of manufacturing, R&D, and sales. The engineers who handle these tasks should increasingly consider themselves data scientists—people mining information that can be used to improve business functions.
The methodologies of advanced analytics are quite different from those used in traditional empirical analysis.To put it simply, traditional data analysis is based on what information you have, advanced analytics on what information you need. The traditional approach usually starts with available data and focuses on the information they reveal and the insights they provide. Project and executive teams then determine how these insights might help them make specific decisions.
With advanced analytics, by contrast, teams begin by asking what business problems they are trying to solve and which critical decisions a company must make. They then identify the insights that will help clarify those decisions, the type of information that might produce the required insights, and, finally, the data sources the organization needs to obtain this information. In a properly designed program, advanced analytics offers not only accurate, reliable, and timely information on past and present operations but also invaluable predictive insights to guide decision making (see sidebar, “Getting more from R&D and sales,” for an example of how advanced analytics can assist with these functions). Companies can use advanced analytics, for example, to create models anticipating future developments, such as R&D bottlenecks that could delay production. With this information, they can make better decisions to direct the business.
In chip manufacturing, the volume of data generated on the fab floor has continued to expand exponentially with each new node dimension. Leading-edge tools have so many measuring instruments that each one routinely identifies and gathers over 300 sensor inputs. In consequence, all information collected throughout the fab—including metrics for processes, products, and machine state—will quickly exceed terabytes of data. Fabs also gather extensive in-line, end-of-line inspection, and metrology data. Few, however, combine and apply advanced analytics to all these production data, even though that could improve many important manufacturing dimensions, including yield, throughput, equipment availability, and operating costs.
Consider, for example, a fab that wants to decrease equipment downtime. The fab could conduct a multivariate analysis to enhance condition-based monitoring—a maintenance strategy that involves examining certain indicators to determine if equipment performance is decreasing. Among other benefits, the analysis would help the fab to predict more accurately when parts or consumables will fail.
With this information, the fab can optimize the planned maintenance schedule, which will reduce downtime, as well as costs for parts and labor.
In addition to preventing equipment failures, fabs can use advanced analytics for more complex purposes. For instance, they could link equipment and process-level data to inspection and metrology data to make more accurate predictions about yield failures or yield degradation. Predictive modeling is difficult, since it requires multiple steps. Fabs must first gather complete data sets and then apply algorithmic approaches to identify patterns in the data before building any models. However, the payoffs can be great. Take the case of a company that recently used advanced analytics to predict process failure in a production step that involved depositing material on a wafer. The company was able to make the prediction with a confidence interval of about 70 percent—a level that might seem low but is comparable to the results obtained when oil, gas, or mining companies apply advanced analytics to their processes. By identifying the factors responsible for failure, the analysis helped prevent significant yield loss early in the production process.