There's been significant debate about whether Moore's Law rings true in today's semiconductor market. It seems improbable for companies to continue developing ever-smaller chipsets while increasing performance, and of course, there is much debate on whether it is a viable "Law." However, one area that is doubling every year at the pace of Moore's law is the growth of manufacturing data available from semiconductor operations.
With the Internet of Things (IoT) ramping up chip demand from smartphone to automotive manufacturers, semiconductor companies have a significant challenge to manage chips within and well beyond the manufacturing process. With this fast moving chip market and the billions of devices being manufactured and deployed, defective device returns or RMAs, have become a central issue to profitability. Manufacturing data is becoming very complex and cumbersome to collect and store, let alone analyze. The question is whether internally developed data analytic solutions can continue to meet the demands of its users within semiconductor operations and provide the actionable information needed to ensure that they can achieve yield, quality and productivity targets.
Faster data access enables better decisions in global manufacturing ops
Semiconductor operations and executive management are collectively interested in capturing and analyzing all the data sets across their company's global supply chain to improve yield, quality and productivity. For years, it was generally acceptable to analyze manufacturing data days or weeks after chips were tested. Due to the data latency involved, the primary outcome of data analysis was to safeguard against future manufacturing or testing errors, but could not address concerns with devices that had already left the manufacturing floor. Some bad chips escaped into the supply chain and many good chips were labeled as "bad" and thrown away due to a multitude of faults with either test equipment or test programs. This was an accepted outcome because it was the only solution available. With the introduction of real-time big data analytics, semiconductor manufacturers can now make actionable decisions to prevent test escapes (bad chips passed off as good chips) or reclaim yield (good chips labeled as bad chips) while it is still possible during the manufacturing process to improve quality or yield respectively.
Collecting and managing dispersed data
In general, semiconductor companies today have globally dispersed supply chains. As a result, wafer manufacturing, wafer sort and final test are often conducted in various countries and even with different subcons, which leads to the generation of fragmented data across their manufacturing operations. Until the emergence of big data solutions, semiconductor companies found themselves having to make wide-ranging business decisions based on data that was inconsistent, incomplete and in many cases, out-of-date as well.
By the time test data, generally standard test data form (STDF), is collected from all the testers at the foundries and OSATs within a given semiconductor supply chain, the opportunity to analyze the data and make meaningful decisions has passed. As a result, many companies have just accepted the ongoing problems of yield loss and test escapes that could have been avoided if the source of the manufacturing problem was found sooner.
Detecting & acting on real-time big data analytics
Better manufacturing operations decisions lead to a healthier return on investment (ROI). And, unlike discrete test-based solutions, a big data solution analyzes manufacturing data directly from every tester in a global supply chain to allow companies to make actionable business decisions within minutes of test completion. This process markedly improves yield, quality and productivity.
Proactively and automatically mining data in real time is equally important. The ability to automate data rules and algorithms enables engineers to easily identify problems which then frees them up to focus on finding the root cause of a problem. Immediate returns in test time reduction, yield recovery and escape prevention are all derivatives of investing in a big data solution.
Another benefit of big data solutions is other business units can find unique value by accessing the same data in real time. Operations managers, test engineers and product planners now can take full advantage of a consistent, complete and accurate dataset and use it in their daily tasks to improve overall business operations.
The big data upshot
Whether a semiconductor company wants to improve yield, quality, or productivity, big data solutions can help address the problems of data collection, detection and action to provide substantial business value. Today, many of the world's largest IDM and fabless semiconductor companies are harnessing the power of big data analytics to help them collect and analyze their manufacturing data. These companies are using big data solutions to safeguard their investments in producing high-quality chips while simultaneously augmenting profit margins and market share.