Can AI Tools in the Cloud Improve Semiconductor Manufacturing?
Google Cloud’s newest solutions are geared to improve productivity, yield, and ROI in semiconductor fabs.
While engineers create some of the most sophisticated technology in the world, certain processes to manufacture these designs remain rather primitive. All About Circuits contributor Ingrid Fadelli previously explored the curiously low-tech side of high-tech manufacturing, in which she interviewed a number of experts in the field to discuss whether these processes could actually be automated and why some of them are likely to remain as they are.
One field that stands to benefit from the developments of Industry 4.0 is semiconductor fabrication. Recently, Google Cloud released a new Industry 4.0 solution for manufacturing operations poised to make long-term improvements in semiconductor production and supply chains.
All About Circuits had the chance to speak with Simon Floyd, industry director of manufacturing and transportation at Google Cloud, and Jason Ruppert, COO at Phononic, to hear how machine learning is revolutionizing semiconductor factory floors.
Google Cloud’s Brings AI to Factory Floors
Google Cloud's newest cloud-based tool was designed to connect factory floor equipment to the cloud and provide monitoring, analytics, and AI/ML insights. Among the many tools possible through Google Cloud’s new solution, some of the most notable include predictive maintenance, anomaly detection, and vision capabilities.
“Our goal here is to allow people to connect their products and their factory so they can have a single view—a single pane of glass end to end—from how something is manufactured to how it operates in the field, and be able to learn from that,” Floyd said.
Predictive maintenance can limit downtime and improve factory productivity. Image from Ixon
On a lower level, the tool is entirely software-based (i.e., Google Cloud doesn’t provide any hardware), and consists of two major components. The first component, Manufacturing Connect, physically connects factory equipment to the cloud. To make the solution as interoperable as possible, Google Cloud teamed up with Litmus Automation to equip Manufacturing Connect with 250 different types of machine drivers for connection.
The other solution is called Manufacturing Data Engine, the cloud component that performs all the data storage, processing, and analytics. This solution can be deployed as a hybrid cloud solution, where users can select which components to run on the cloud and which to run locally, adding flexibility and customization to the mix.
“To be a hybrid cloud, some components run directly on hardware so they can be installed in the factory. Then, other components go to the cloud,” Floyd explained. “The way we balance that is the difference between the scale of data processing and the speed of data acquisition. From here we can make the decision as to where something should run or operate.”
Phononic’s Deploys Google Cloud Solutions
So far, this tool has been used by a select group of companies, including Phononic, a manufacturer of thermal electric technology. Phononic has put Manufacturing Connect and Manufacturing Data Engine to work on its factory floor for two months.
Phononic makes solid-state thermoelectric devices. Image from Phononic
“There are a lot of complex processes that we want to be able to see in real-time,” Ruppert said. “For something like a wet etch or a plating process, there are variables like PH balances in assembly. There are pressures on machinery. Whatever the case may be, we want to be able to see those processes in real-time.”
With the new tools from Google Cloud, Ruppert said Phononic has turned monitoring and analysis into insights. For example, the solution can provide predictive maintenance, allowing Phononic to shut down and repair a machine before it becomes damaged beyond repair.
Speaking about the benefits that Phononic has actualized through Google Cloud’s solution, Ruppert noted, “There is a significant ROI with just looking at this one part of our fab. Beyond that, we have a clear line of sight that our yield and throughput in this one particular area are going to improve dramatically.”
Example of data journey from the factory floor to the cloud. Image used courtesy of Google Cloud
“The worst job in a factory is where you're the substitute for a machine. You have to do monotonous things by hand, and it's kind of degrading to the human race in a way,” Floyd added. “We'd like to give [operators] better instructions for how to perform a function, so that they're adding their own value in the right way. We want them to be the human in the loop. When it comes down to a machine not quite understanding whether something is good, bad, or indifferent, a person can help the AI be trained using that human knowledge.”
In complex manufacturing environments within the semiconductor industry, factory operators must monitor a number of variables. By bringing cloud-based solutions with the AI/ML capabilities to the floor, Google Cloud anticipates that its new tools will improve yield, efficiency, productivity, and ultimately cost.
“We're not talking about something that's a bespoke solution or something that's very specific to one factory,” Floyd said. “It can be repeated over and over—something that works at the enterprise level. You can onboard a factory very easily.”