The computer chip shortage has prompted Dr Geert van Kollenburg and his colleagues at Eindhoven University of Technology, the Netherlands, to find data-driven methods to optimise chip manufacturing processes.
As part of the MadeIn4 project, they have developed a predictive discarding framework in which quality predictions from artificial intelligence (AI) algorithms are used to decide on whether to discard an unfinished product. This approach can improve both the profitability and sustainability of manufacturing processes.
Read more in Research Outreach
Read their original research: https://doi.org/10.1080/09537287.2022.2103471
Image source: Phonlamai Photo / shutterstock.com
Hello and welcome to Research Pod! Thank you for listening and joining us today.
In this episode, we will highlight the predictive discarding framework developed by Dr Geert van Kollenburg and his colleagues at Eindhoven University of Technology in the Netherlands. Using innovative artificial intelligence algorithms, the framework improves the profitability and sustainability of computer chip manufacturing.
Our digital society relies on computer chips, but these chips are currently in short supply. As this is due to a combination of global events, the scarcity will remain for the foreseeable future. If the supply of chips is to meet the demand, chip manufacturing processes will have to be optimised in new ways.
Dr Geert van Kollenburg and his colleagues at Eindhoven University of Technology in the Netherlands have developed predictive discarding, which combines industrial statistics and prescriptive analytics to predict whether a product will meet the required quality standard. If the model indicates that this quality standard is not going to be met, the unfinished product can be discarded without completing the manufacturing process. This significantly reduces the waste of resources, such as energy, time, and raw materials, which would otherwise be used to complete the faulty product. This increases the throughput of factories and reduces environmental footprints.
In their papers, van Kollenburg and his collaborators explain how, in addition to reducing the time required to produce a certain amount of good-quality chips, predictive discarding can contribute to the overall reduction of the carbon footprint generated by wafer manufacturing.
The accurate alignment of the many layers of a wafer is closely monitored throughout the manufacturing process. The research team trained classification models to use misalignment measurements to predict what the quality of the wafer would be if it would be completed. They discovered previously unknown relationships between misalignments and the quality of the end–products which could be used to make a decision on whether to continue manufacturing a wafer or discard it.
Predictions are not always perfect, so it is possible that even when predictive discarding is used, false positives occur, and some good-quality products will be discarded. Likewise, some poor-quality products can make it through to the end of the production process. In both cases, resources are wasted. This led the researchers to investigate when predictive discarding can benefit manufacturers and to identify which conditions are required for its successful adoption.
This revealed that even when recalling 50% of all products, predictive discarding could reduce the total resource consumption by 9% if the decision to discard is made early on in the manufacturing process.
Of course, many critical faults in manufacturing processes are already known by the manufacturer and the people responsible for process and quality control. The use of AI in quality predictions can help the decision-making process in discarding faulty unfinished products by finding previously unknown combinations of factors that affect the quality.
Complementing the human contribution to manufacturing processes with AI is a fundamental step in the evolution to Industry 5.0. This research shows that including resource consumption in data-driven techniques can optimise the decision-making process. The researchers believe that the investment of manufacturers in resource-aware data-driven methods can improve both the profitability and the sustainability of manufacturing processes.
Van Kollenburg and his collaborators present predictive discarding as an asset for manufacturing processes in general and can help to control production. Implementing predictive discarding requires only limited resources and its benefits can be explored with data that are already available from standard process measurements. They conclude that ‘predictive discarding may therefore become standard practice in Industry 5.0, where AI and humans work together to achieve sustainable production.’
That’s all for this episode – thanks for listening, and stay subscribed to Research Pod for more of the latest science. See you again soon.
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