Inefficient data silos threaten to overwhelm many manufacturers- luckily there’s hope.
Semiconductor companies are leading the way in transforming factory floors to Industry 4.0. They have been generating and analyzing data with the newest technologies for years. Now, Artificial Intelligence (AI) can help companies gain insight into effectively improving fab operations and drive lower maintenance costs and higher yield.
DataOps, or the process of combining and cleaning data, has been a key impediment to implementing Industry 4.0 projects, even for semiconductor people who know and value clean data. One difficulty is that machines report data only to a database built by the same vendor, requiring engineers to spend hours or even days manually downloading information from silos to correlate it with other data in order to glean the needed information. This costly and time-consuming process may deter companies from fully exploring data insights to better optimize factories.
Additional DataOps challenges semiconductor manufacturers face include:
- Tools used to solve problems on a small scale are ineffective on a large scale
- Belief that data in silos is already clean and is being utilized at max value
- Belief that current methods of data collection are the most cost-effective
Maximizing the value of a company’s own data can mean the difference between acquiring new customers and even retaining current customers. Semiconductor manufacturers collect and record significant amounts of data, but it’s often collected for a different reason than they need later in the process. An omniscient manufacturer would never guess wrong on the end-need for data, but because it’s impossible to predict all potential needs and uses for data, the current process is incomplete and may introduce significant flaws into data collection and sorting.
Let’s use semiconductor wafer tracking as an example. A semiconductor company stores detailed data in a wafer-tracking manufacturing execution system (MES). The data was captured to control process parameters during wafer processing, so it was associated with individual wafers as they progressed through the line. The data was very clean and sliced up for the intended purpose. Now, the company wants to use that same data to track the health of the underlying machine, but they soon find out they have a huge problem. They associated the data with a wafer rather than a machine, so it becomes very complicated to determine what happened to the actual machine over time. Just getting started requires finding every wafer a machine processed, gathering the data for that machine from each wafer data silo, and stitching it together to get a machine-focused picture instead of the collected wafer-focused picture. After doing all this, they still only have a partial picture because they lack data from when the machine was on but not producing wafers.
Software alone cannot fix this problem, nor can hardware. By contrast, the right Industrial Internet of Things (IIOT) platform does fix the problem because it scavenges the data from machines on the floor and passes it to a data broker for real-time analytics, converting raw factory information into business value. In an increasingly competitive global marketplace, the ability to satisfy customers’ demands for faster, more powerful metrics is a dominant competitive edge. Data is being increasingly used to solve process issues, which is critical as many new product introduction lines are asked to ramp up production rapidly and they need to solve problems faster.
In semiconductor manufacturing, speed and yield are of primary importance. The concepts applied to improve these qualities in fab operations are the same as those used to enhance data standardization and output. In both situations, correct tools are key. With automation in place, and with the right tools to properly tee up the raw material (data), it’s an open lane to increasing return.
Tim Burke is co-founder and Chief Technology Officer of Arch Systems where he works to accelerate Industry 4.0 by standardizing connectivity and data gathering across the factory. He has broad expertise in industrial communication protocols as well as the challenges of working with the diverse set of machines found in electronics and semiconductor factories. Tim’s published work on the device physics of organic photovoltaics has been cited by thousands of researchers.
Arch Systems‘ unique technology retrofits new and legacy machines to extract industrial machine data and propel AI and Industry 4.0 transformations. The ArchFX Broker streamlines Industry 4.0 implementation and drives greater ROIs by running one application across all machines. Because it integrates with SCADA, MES, and ERP, it is Machine Learning and AI-ready. This results in an answer to one of the most common challenges in manufacturing data analysis today: providing a well-marked path to identify, optimize, and automate processes.