Industry News, Trends and Technology, and Standards Updates

The amount of data generated in a modern wafer fab is astounding – a terabyte of data can be produced in mere moments. And while the technologies harnessed to build modern chips are at the absolute bleeding edge and have been compared to the sacred by some reviewers, the technology used to gather data and monitor factory equipment is often, surprisingly, not.

Ripping out and replacing shop floor applications is rarely done. And so, when a fab is built, its data acquisition and control architecture is generally as permanent as the bulk gas transports often seen hulking on the exterior. Modern software architecture has changed dramatically over the last 10 years. Yet the fab and its support systems are relatively static as compared to the process equipment needed to build the latest chips. The result of this imbalance and inertia is that fabs are full of equipment generating oceans of bits and bytes while the managers are struggling to stay afloat and man the oars. This problem only gets worse as management climbs the corporate ladder, or the mast, to extend the metaphor. The people in the corporate suites trying to understand and manage the realities going on in multiple fabs are absolutely inundated and clinging to debris in the deluge. No enterprise-level software system was ever designed to deal with this Noachian torrent of data. Out of this necessity fab managers have been employing data center technology and software architectures more commonly used to run social media sites and Amazon than to run factories.

On the 8th of August 2022, Google Search went down for approximately 34 minutes due to a poorly planned software update.  What is surprising about this is that it had been years since Search had suffered an outage like this, and more years hence. Google’s search page is the most heavily visited site on the internet and yet has one of the best availability metrics and performance history.  There are undoubtedly many reasons for this success, but one of the most acclaimed is Google’s application server: Kubernetes.  Named for the Greek word for helmsman or navigator, Kubernetes has become the standard application server for modern software architectures.  Software purists will undoubtedly object, and say that Kubernetes, or K8s, merely orchestrates the deployment of software, but this is no longer true.  K8s has become an ecosystem that hosts scores of other software products that do everything imaginable from compiling, testing, packaging, deploying and monitoring all the code required to keep a product like Google Search running.  

Incidentally, many of the most successful K8s ecosystem products continue the nautical theme with names like Docker, Armada, and Helm.  

In 2015, Google, in partnership with Docker and others, gifted the K8s technology to the open-source community at the Linux Foundation.  The Cloud Native Compute Foundation (CNCF) project was announced at that time with the goal to unify the rather fragmented containerized approach to software deployments. 

At PDF Solutions and the Cimetrix Connectivity Group, we saw this transformation happening and decided to get out in front of it.  We’d been training shop floor engineers to drink from data firehoses for decades, showing them how to tap into the torrents and pull out just the manageable streams they required and were equipped to handle.  Now, thanks to the CNCF, we can build software that handles far more data and still survives in the 24x7 environments that wafer fab production demands. Cimetrix’s Sapience Manufacturing Hub is our answer to this. 

The Hub solves the problem of getting actionable shop floor data to the top floor for the semiconductor industry, akin to navigating between Scylla and Charybdis. Because the most complex wafers take 3-4 months to process through a fab with the number of process steps in the thousands, even dealing with a single wafer lot of data has proved to be an enormous challenge.  Consequently, cost data associated with materials, labor and rework are allocated equally across all the products in the fab over many months. The result is that detailed data about how much a particular wafer costs or the profit margins of a product are buried in the averages.

The Hub is used to gather clean data from the factory floor and aggregate it where it makes sense. Milestone processes are used to report aggregated data by product, order, lot and other relevant factors so that costs can be accurately accounted for at the enterprise level. When the costs for energy, materials and testing go up while yields fall, knowing the details is important.  The Sapience Manufacturing Hub is the first cloud-native platform that can scale using common and well-known data center tools and provide this data in a way that is useful to the corporate suite of applications.

If you want to know more, please reach out to us by clicking the button below. We are a group of engineers committed to working on the biggest and most insoluble problems facing the electronics industry and really enjoy collaborating about all things semiconductor and data science.  

There are some of us in the software development community who recall when cloud computing was not much more than a marketing buzzword, mocked by many developers with first-hand experience at the pace of change in the internet age, but maybe not quite enough experience to know better. Today, cloud-enabled architectures are so commonplace that it’s the alternatives that must be defended in most quarters. Although not necessarily in manufacturing.

In parallel to cloud computing, Industry 4.0 and Smart Manufacturing are happening, and the effects are becoming more apparent and impossible to ignore. Fewer people are mocking I4.0 and Smart Manufacturing as buzzwords. More often, they are being better defined as a set of useful principles and applied to real-world problems with exciting results.The confluence of I4.0 and cloud computing is a rather rare intersecting set. For many manufacturers, it’s a bit much. Those of us working in this area understand the famous quote (mis)attributed to Mahatma Gandhi: “First they ignore you, then they mock you, then they fight you, then you win.” The fight is underway; the confluence of cloud computing and Smart Manufacturing are the focus of this writing.

During the Industry 3.0 changes, when computers were introduced in a significant wayts themselves all changed. Now that Industry 4.0 is upon us, it is the networking of these machines that is driving the change. The network effect is easy enough to understand, but the resulting change is bound to have ripple effects across the industry that will be hard to predict.

Something similar happened a decade ago with cloud computing. At first, the strategy and benefits were understood as simply renting compute power from a third party. “Cloud is just someone else’s computers” was a common refrain among IT professionals. This was true enough at first, when moving to the cloud was done as a “lift-and-shift” strategy. This meant you should take one app, run it on similar platforms in the cloud, save a few bucks, repeat. However, very quickly some very innovative companies realized that the flexibility, scalability and number of new services provided by public cloud vendors meant that applications would have to be re-architected to exploit these possibilities. The software industry is still discovering all the possibilities of the resulting SaaS models. Salesforce, Netflix, Amazon and a few others saw the possibilities, built their apps and services, and the rest of us are still learning.

At Cimetrix we have some experience working with manufacturers who are stepping into this area of I4.0 / cloud confluence. Our sense is that the conversations occur along the similar lines of pursuit. The first topics of conversation revolve around fear – security being the chief concern. How can a factory allow its data to leave the four walls? Two recent events have made this argument easier to overcome: TSMC had to shut down a major part of its operations in the summer of 2018 when a computer virus, installed on a new tool, spread to many other hosts. Hundreds of millions of dollars in shipment delays and other costs resulted from a breach of what had previously been thought to be a secure factory environment. On the cloud side: The Capital One breach, where one million social security numbers were stolen, had initial headlines that related it to the Amazon cloud. Since then, the bank has admitted fault and it has become clear that AWS services are secure.

Two critical elements important to the security argument are 1) employing talented security professionals and 2) deploying critical security patches as soon as vulnerabilities are discovered. The public cloud vendors recognized this long ago and hence their data centers employ security measures beyond the affordability of most business. Factories that continue to host their applications on premises, as opposed to the cloud, are increasingly competing with cloud vendors for security talent. These cloud vendors have massive scale and are still growing at ~40% per year. The result of this is that your apps and data are increasingly safer in the cloud than on an “on-prem” server.

Once these fears are assuaged the next line of reasoning tends towards identifying opportunities. This is where Cimetrix is uniquely positioned. We have the expertise to connect factory equipment, get the data into the cloud, and show our customers how to begin exploiting these technologies. Very often the first step is simply to connect as much factory equipment as possible, get a few simple messages, and expand later. This option has proven very fruitful for distributed supply chains that utilize contract manufacturing and outsourcing. Knowing the rate at which equipment is being utilized, which can be done with as few as two simple messages, can be extremely useful. Negotiating capital budgets for new products tends to improve when utilization rates for existing equipment are well known to all parties. The ROI for projects like this tends to be of the scale of months or weeks, not years.

After proving the ROI this way, with only a few simple messages, the next steps typically involve gathering more data. This is where the real power of cloud computing can be brought to bear. Smart factory computing implies the application of intelligence at the factory level to create a dynamic production environment where reducing costs and improving quality happens extremely quickly. Machine learning and very good AI tools are being developed now by the public cloud companies and to this author seem to be perfectly suited to factory data. “Big data” doesn’t get much bigger than the myriad of sensors already at work in a typical factory, pumping out immense amounts of data. Getting this data into the cloud and closing the loop back to factory equipment will benefit the first adopters in ways similar to the early cloud computing innovators.

Ten years ago innovative companies made a kind of leap, and re-architecting applications for the cloud brought large benefits. We see a similar leap coming for manufacturers who are willing to innovate with the help of these new cloud services. It’s not difficult to imagine how Amazon’s ecommerce engine has benefited from customer data to recommend just the right brand of beer to an on-line buyer of a Manchester United t-shirt. A data scientist I knew once said, “the algorithm says that when it’s raining in England we should recommend this beer. I don’t care why as long as it sells.” This same algorithm is on its way to a factory near you. Although instead of online conversions of browsers into buyers, these algorithms will be tweaked to focus on yields, cycle times, and utilization rates.

There are many other arguments for cloud computing which we ignore here. Arguments in favor of availability, scalability, compliance, ease of deployment, etc. These are all true but better addressed in many other venues. This is likewise the case for Industry 4.0; it is a younger sibling topic as compared to cloud computing, but still better fleshed out in other writings. We at Cimetrix are confident that when we look back 10 years from now, the companies that innovate best at this confluence of technologies will realize an immense potential. 

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