Reimagining fabs: Advanced analytics in semiconductor manufacturing
Fabs want to streamline the end-to-end process for designing and manufacturing semiconductors. Will innovative analytical tools provide the solution they need?
Across industries, the application of advanced analytics, machine learning, and artificial intelligence is disrupting traditional approaches to manufacturing and operations. While semiconductor companies have been somewhat restrained in applying these technologies, that may soon change—and with good reason. Lead times for bringing integrated circuits to market have been gradually rising with each node. New design and manufacturing techniques account for some of the increase, but more complex inspection, testing, and validation procedures also create delays.
A quick look at the semiconductor value chain shows that fabs need help in multiple areas (exhibit). There has been a 50 percent increase in test and verification time during the design process over the past few years, and new-product introduction and ramp-up now generally involves 12 to 18 months of debugging. Similarly, 30 percent of capital expenditures during assembly and testing relate to tests that do not add value. The problems don’t stop after chips enter the market: customers may encounter unexpected performance issues and ask semiconductor companies to help resolve them—a difficult task, since there’s no way to trace a chip from design through use. What’s more, many fabs don’t have efficient processes for recording problems encountered during production, or the steps they took to resolve them.
In many cases, problems arise because important tasks still require frequent manual intervention, despite having some degree of automation. To improve the process, many technology companies are now creating analytical tools that could help fabs replace guesswork and human intuition with fact-based knowledge, pattern recognition, and structured learning. In addition to reducing errors, streamlining production, and decreasing costs, these tools might even help fabs discover new business models and capture additional value.
Although analytical tools are just beginning to gain traction at fabs, semiconductor players already have many options from which to choose, since many technology players have recently developed specialized solutions to streamline the chip-manufacture process. We chose three companies from the large pool of innovators to serve as representative examples of nascent disrupters, interviewing their business and technology leaders to gain further insights into their capabilities. Our goal here is not to endorse companies selectively but to provide diverse examples of emerging solutions for semiconductor companies that might be unfamiliar with the new offerings.
Advanced data analytics now offer fabs an opportunity to test and flag possible points of failure in virtual or digital-design files. Companies can then correct errors in physical designs and improve yield and reliability without running a single wafer or making a mask. Fabs can also use the same techniques to generate and run virtual and actual test chips, allowing them to identify and eliminate marginalities while simultaneously optimizing processes. Finally, advanced data analytics allow fabs to combine numerous inputs from sensor and tool data with extensive process-level information to create a rich, multivariate data set. They can then rapidly isolate and amplify possible sources of chip or equipment failure, giving them an early warning of potential problems. The tools can learn from prior designs and enhance their ability to detect failures over time. To gain more insight about new tools that may prevent errors, we spoke with Bharath Rangarajan, CEO of Motivo, an advanced-analytics company that has enhanced the approach to predictive analytics by using proprietary algorithms, machine learning, and artificial intelligence to provide greater insight into diagnosing and preventing complex chip failures.
