Now that relative stability of the global semiconductor supply chain has returned, the reaction to chaos has been to find someone to blame: Surely, someone must have realized a serious disruption was coming. But the reality is, this has been a perfect storm of compounding factors, and it’s arguable that anyone had the clairvoyance to predict that almost everything that could go wrong would do so and at the same time.
So, rather than looking for scapegoats, the chip market now has the opportunity to reevaluate how it conducts itself to prevent this from happening again, or at least mitigate the consequences if it does. It starts with data.
Analytics solutions have been widely promoted yet rarely implemented in the flag-waving about Industry 4.0. Even though equipment manufacturers spend millions developing software to support their tools, and chip manufacturers spend equal amounts purchasing software from third-party vendors, neither solution provides a “one size fits all” answer.
Instead, manufacturers have resorted to creating software solutions themselves and piecing together solutions on multiple, often incompatible platforms. This approach is labor-intensive and time consuming.
And, even with multiple software solutions in place, many chip manufacturers don’t have the advanced analytics capabilities that could ultimately increase efficiency and improve yield. Nearly three-quarters of chip manufacturers have software tools that provide diagnostic capabilities, but they have yet to realize the benefits of predictive or prescriptive analysis.
That is, while information about equipment operations and potential failures may be available, the information isn’t precise or clear enough for operators to act on it. Faced with information like this, many find these tools frustrating, confusing and overly complex.
Semiconductor manufacturing, logistics, and distribution are also exceedingly complex. Consequently, chip manufacturers must slog through long, tedious manual processes to apply analytics to obtain the intelligence from all their data. Using multiple platforms with proprietary code bases only creates barriers to getting the required analytics capabilities and intelligence when needed, which is increasingly “right now.”
Aggregating these variables within a single ecosystem would eliminate data silos characterized by trade secrets and proprietary intelligence. Currently, it’s difficult to know what everyone (and everything) is doing and seamlessly coordinate it. The chip market would benefit from open, agnostic data management and analytics.
For example, chip manufacturers could apply analysis and AI-based solutions to any group of processes or equipment in the production line. A customizable solution — from the dashboard to data management to analytics and intelligence — would enable seamless workflows that are tailored to the unique needs of each chip manufacturer.
It’s conceivable that such a platform could become an industry standard for monitoring and analytics in the semiconductor industry. Open environments are not new, but they’ve become popular in large measure because they allow secure collaboration among industry members with the assurance that critical IP will not be revealed. An open platform could become a secure collaborative solution to give a community of users and chip manufacturers the ability to leverage the experience of others who have solved similar problems.
Of course, any platform would have to be equipped with significant security features so that users could create custom solutions and keep vendor-specific IP secure. It would also require an agnostic approach to programming languages, easily accommodating all or most of them.
The semiconductor manufacturing industry currently suffers from a fragmentation of monitoring and analysis. This hinders the its ability to respond to challenges produced by supply chain interruptions that will almost certainly appear again in the future. An open analytics framework could go a long way toward reducing costs and development time in the chip market and provide actionable insights that were never identified before.