An open software platform to bolster IC manufacturing yield

Article By : Yuji Minegishi

IC manufacturing is well served by a platform where all analytics, fault detection, optimization, and communication occur in a single place and with the depth of insight that only vendor-specific intelligence could provide.

As the pandemic exacerbated what was already arguably a formidable supply chain problem, the semiconductor industry continued—as it has for years—to focus intensely on discovering ways to increase efficiency, precision, and yield. The pandemic and rapid consumer buying enthusiasm simply functioned as an accelerator to hammer home what the industry already knew: swift advances are required that facilitate increased productivity.

Among the most promising approaches for achieving higher yield within the semiconductor industry is greater reliance on software and collaboration throughout the industry to cohesively integrate fragmented and siloed processes that inhibit progress.

While software for equipment monitoring, problem troubleshooting, data analysis, and report generation is in place throughout the industry, the software solutions are typically either home-grown or based on an assortment of solutions from multiple vendors. As the resources available to any given manufacturer are limited, it can take months to install new features and enhancements. Even after repeated requests, the new software capabilities often never actually appear. With solutions from multiple vendors in place, the onus is on the IC manufacturer to contact each individual vendor, update all of the software at play on the production line, and then hope that everything is compatible and works seamlessly together.

According to participants in a recent survey (Figure 1), professionals in the semiconductor manufacturing chain want tools that are easier to use, more intelligent, able to support all their equipment, can automate the process of reporting and alerting, and are verifiably secure. Yet when manufacturers attempt to integrate equipment monitoring performance on their own, the results can be lackluster.

Figure 1 A survey conducted by Gigaphoton provides insights into the current state of the semiconductor manufacturing industry.

Prescriptive analytics

Among the biggest offenders are analytics tools that offer basic descriptive and diagnostic features but don’t effectively address predictive and prescriptive maintenance. Predictive maintenance uses statistical and modeling techniques to determine what may happen in the future based on historical data and attempts to determine when a machine needs servicing. What it does not do is determine what action should be taken; it simply informs the user that maintenance is needed. The predictive maintenance approach is slowly being integrated into manufacturing sectors within the context of Industry 4.0.

In contrast, prescriptive maintenance, while less prevalent and well developed, takes analysis a notch higher. It not only predicts failure events in equipment, but can recommend actions as well. Where predictive maintenance provides information to determine whether to perform or defer asset maintenance, prescriptive maintenance offers a suite of options and outcomes from which to select. In the future, the prescriptive approach may even schedule maintenance and assign the best available technician or team to make the repair or replacement.

Prescriptive analytics powered by artificial intelligence (AI) tools could even assist maintenance teams by providing detailed instructions while the remedy is being deployed. With the vast amounts of equipment and sensors within a typical fab, both approaches can improve every manufacturing process as they minimize the possibility that some vital component could fail or be degraded before timely action can be taken.

Figure 2 The four levels of possible insights are provided by analytics tools that range from descriptive to prescriptive. Source: Gigaphoton

Common denominators

What’s interesting about this scenario of predictive versus prescriptive maintenance is that while every semiconductor company’s processes are different and proprietary, they have many common problems. To find a solution, it would make sense to have a single software platform that would allow all equipment manufacturers to collaborate. That is, it would give chip manufacturers the ability to leverage the experience of others who have solved similar problems.

Of course, such a platform must ensure that participants’ intellectual property (IP) would be inherently secure, and similar efforts in other industries show that this goal can be achieved. With this assurance in place, the platform would allow cross-vendor equipment monitoring and analysis to be performed while enabling each vendor to solve their specific problems. The ability for customization would be essential to satisfy the unique needs of each participant. The platform also should be agnostic to various programming languages and accommodate all or at least the most prevalent languages. Another benefit of the platform would be more effective use of AI integration within critical processes.

This collaborative approach is best accomplished on a single open platform that eliminates the need to rely on products from multiple, often incompatible, sources. It is likely that downtime and service calls could be reduced because manufacturers could quickly and easily determine for themselves which tool is causing the problem, and it would be possible to determine that some issues might be cross-functional. The approach would dramatically increase efficiency and yield that, in turn, could drive up the rate of production.

In short, advancements in software tools have become essential for the semiconductor industry to accelerate time to market. But the sheer number of these tools, both developed in-house and supplied by third-party vendors, has also become a hindrance to the goals the industry is trying to achieve. As this article demonstrates, an efficient way to harness all these tools is to share information about them among competitors without revealing the “secret sauce” that is the core of each company’s success.

A single software platform

The semiconductor industry’s current fragmentation of monitoring and analysis tools hinders its ability to respond to challenges produced by supply chain interruptions that will almost certainly appear again in the future. Emerging solutions like Fabscape, a new open platform imagined by the laser manufacturer Gigaphoton, provides actionable insights that were never identified before.

Figure 3 A rendering of Fabscape, a new open platform concept that aims to bring together streamlined data monitoring, management, and analytics across the production line.

Such open, collaborative platform solutions are not new, and even the Department of Defense (DoD) increasingly employs them to encourage greater integration with the private sector. Although the DoD initiative faced early hurdles as companies feared their IP would be compromised, assurances put into place slowly made them more comfortable with collaborating with their competitors—and with the government. The key to their success has been collaboration, trust and mutual benefit, and there is no reason why something similar cannot be used by the semiconductor industry.

In summary, to increase productivity on an industrywide scale, semiconductor manufacturing would be well served by a platform where all analytics, fault detection, optimization, and communication occur in a single place and with the depth of insight that only vendor-specific intelligence could provide.

This article was originally published on EDN.

Yuji Minegishi is the general manager at Gigaphoton Inc., a manufacturer of light sources for semiconductor lithography processes. He leads the engineering and development efforts for Fabscape.


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