The 10 Biggest Data Challenges of the Semiconductor Industry

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... and How a Yield Management System Helps Solve these Problems

Semiconductor manufacturing generates enormous volumes of data. Gigabytes. Terabytes. Petabytes.

Global semiconductor manufacturing hit record highs in 2021 and the abundance of data along with it. While semiconductors themselves are becoming more and more complex, more sophisticated testing equipment and the presence of sensors at every step of the long and complex manufacturing process contribute heavily to the avalanche of data being collected year for year.

Fabs inspect 100% of their wafers and die, flooding systems with test data. Some companies must also archive all of their data for 10-15 years.

The capacity to store and retrieve terabytes of data is just the tip of the iceberg as it turns out, because data alone is of limited use to anyone without context, correlations and traceability.

Here are 10 of the biggest data issues semiconductor manufacturers must contend with:

1. We have huge amounts of data, most of which isn’t even being looked at. A large chunk of our engineering time is spent just gathering and uploading the data.

Dealing with the magnitude of data is a daunting task. In-house solutions and various bits of pieced-together software are time-consuming and cumbersome, and still don’t provide access to all the data that is being generated.

The yield management system YieldWatchDog was specifically created for semiconductor manufacturers with Big Data in mind. Our standardized data integration service quickly loads even large amounts of data.

By integrating all available data sources into one, large scale, highly-compressed database we create the basis for applying our statistical and artificial intelligence (AI) algorithms to detect outliers, anomalies and abnormal data.

Engineering hours can then be spent solving problems that were detected by the software and making smart, data-based decisions.

2. We are a fabless start-up.

Our yield analysis solution is completely scalable, whether you are collecting gigabytes or terabytes of data.

3. We are a fast-growing company.

Dealing with data issues early on by investing in a scalable yield management system will set you on the path for success so once your company’s growth takes off, you will be agile and able to react quickly to changes in the market and demand, even as you are generating more and more data.

4. We have data from different sources and in different formats.

Data integration is perhaps the single biggest data issue for semiconductor manufacturers, impeding engineers from applying any analytics. Companies have data coming from multiple sources: test data (inline data, parameter test, binning, WAT/PCM, wafer/die sort, final assembly/test, defect data), engineering data and data from outsourced processes.

There is a lack of a common format that is used even within a single fab. STDF, the Standard Test Data Format, has never been used consistently and isn’t sufficient anymore for the increasing complexity in testing. The coming standard RITdb has not yet been widely adopted.

Also, because data is coming from multiple sources, it often isn’t properly aligned or correlated.

Before any analysis can be applied to the data, it has to be painstakingly cleaned, parsed and merged.

DR YIELD has developed its own standardized data integration process for the loading of data, automatically converting and smoothly integrating different types of data. Data is transferred quickly and easily.

Merging multiple data sources in one tool makes it infinitely easier to make correlations and identify yield issues.

5. We have many people looking at the data. Executives, yield engineers, production engineers…

YieldWatchDog, with its intuitive, user-friendly interface, enables useful data analytics across operations, generates reports and provides actionable insights into your data with the click of a mouse.

Automatic reports can also be generated periodically or per lot and accessed by non-YieldWatchDog users.

6. Root Cause Analysis is critical for us to quickly identify the root cause of yield problems and fix them.

YieldWatchDogXI, the eXpanded Intelligence layer of DR YIELD’s yield analysis software, can assist with root cause analysis. In addition, machine learning (ML) algorithms automatically detect and classify wafers with conspicuous patterns, allowing engineers to pinpoint yield issues.

7. The semiconductor manufacturing industry is continuing to grow under enormous pressure to produce more advanced microchips for more industries – around the clock at highest capacity, with no room for failure.

YieldWatchDog’s algorithms can predict failures based on early knowledge about deviations or anomalies in the data, essentially allowing manufacturers to act on early warning signs instead of reacting to major manufacturing problems when it’s already too late.

The multivariate monitoring feature of YieldWatchDogXI allows you to review the correlation of parameters and thereby detect issues that would otherwise not be visible, enabling you to set corrective measurements immediately.

8. We have very specific needs of how we want to look at and analyze the data.

It’s highly likely that the capabilities you require already exist within our current, proven solution, with its many integrated visualization and calculation tools. If not, feature development to give our customers deeper insights into their manufacturing and test data is just one aspect of our renowned customer support. Our experienced team of developers works closely with your yield engineers to understand your requirements and provide the capabilities you need.

9. We need to trace the genealogy of materials down to the die level.

YieldWatchDog’s data integration enables tracing the origin and movement of semiconductor materials from different sources along the entire manufacturing lifecycle, with full supply chain transparency to lot, wafer or device level.

10. We must deliver the highest quality and performance to our customers.

For our customers in aerospace, automotive, medical and other quality critical industries, our Advanced Quality Module encompasses features like neighborhood rules, part average testing (PAT), value shifts and shot and stack analysis, all conducive to achieving zero quality defects.

Want to know more about YieldWatchDog?
About DR YIELD software & solutions GmbH

DR YIELD provides the leading-edge advanced analytics software YieldWatchDog for analysis and control of semiconductor manufacturing and test data. This enables semiconductor manufacturers as well as Fabless companies to improve important manufacturing dimensions such as equipment availability, throughput, operating costs and yield. Once installed YieldWatchDog allows you to get actionable insights into your data. For detailed information click here 

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