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By Rich Handley

Edwards Selects MapR Technologies for Real-Time Data-Analysis Solution

MapR Technologies, a company that provides a data platform for artificial intelligence (AI) and analytics, has announced that Edwards, a vacuum and abatement solutions firm, is using MapR's data platform to develop semiconductor industry solutions to analyze data from equipment within customer sites to deliver predictive maintenance and near-real-time anomaly detection.

"The manufacturing of modern semiconductors include up to 1,000 process steps with a significant and increasing proportion requiring vacuum and abatement systems and a typical plant may have Edwards assets measured in the 1,000s as critical sub-systems for these processes," said David Hacker, Edwards' strategic marketing manager, in a prepared statement. "Our vision is to have all of these assets delivering near real-time information on their performance and the detection of anomalies that may occur with a deeper level of scrutiny. This allows us to create more evidence based, relevant information on quality of vacuum service to enhance the knowledge provided to customers so that they can quickly adapt to any situation to deliver more efficiency and better quality of process outcomes. Along with this, the data will enable increased and predictable overall equipment availability along with 'adaptive triggers' for scheduled maintenance."

Edwards chose MapR to enable the capture and processing of diverse data from a range of sources, to help it build machine-learning models that will underpin smart systems. "Our current and medium term aim is to include sensor data to enhance the intrinsic Edwards asset data," Hacker said in the prepared statement, "and to be able to make this service available to other critical sub-system providers to create a more granular and broader understanding of the environment that will ultimately lead to adaptive control based on data analytics."

The first deployment of the MapR platform is currently under way in a joint project between Edwards and Fraunhofer-Gesellschaft, as part of a cooperation that involves research, development and evaluation of specific sensor and IIoT technologies, with the aim of generating and securing data to feed advanced analytics. Key to this is research into the correlation between process and pump behavior, to be piloted in the semiconductor cleanroom environment of Fraunhofer EMFT.

Machine-learning techniques will be used to detect anomalies in sensor data. For this purpose, data fusion of several sensor data for combination and pattern recognition, as well as algorithms for detection of specific states of instability, are required. The aim is to improve the process of predictive maintenance by exploring new machine-learning algorithms fed with sensor data from above and below the cleanroom floor, to predict future performance and anomalies.

A secure connection will be implemented to allow data, machine-learning models and equipment status to be exchanged in real time between secure locations—on-premises at the Fraunhofer EMFT CMOS line, and remote in the cloud or at Edwards' headquarters. As well as reducing response time for service, this simplifies analytical model development and deployment. The aim, according to the company, is to demonstrate a new reference architecture consistent with the principles of RAMI 4.0, but tailored to meet the demands for data and IP security imposed by the semiconductor manufacturing sector.

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