Today, the manufacturing sector is undergoing rapid transformation thanks to a combination of AI and advanced technologies such as the Internet of Things.
The transition was so significant that it has been considered the fourth industrial revolution; commonly known as Industry 4.0. The result is highly digitized and connected smart factories, capable of operating almost autonomously and, thanks to AI, self-correcting.
Machine learning algorithms and applications offer manufacturers access to new, disruptive business models, allowing them to optimize their operations and improve the quality of their products, while reducing their costs.
Proactive companies are preparing for the change in their competitive landscape and adopting machine learning, but is it really worth it?
Here we look at a case study that shows the dramatic effect that machine learning impacts manufacturingand more particularly on improving efficiency in the semiconductor industry.
The problem of yield losses in the semiconductor industry
In complex manufacturing environments, such as those in the semiconductor industry, production processes typically include multiple steps and can span weeks or even months. Yield losses, when products must be reworked or scrapped due to defects, have a significant impact on production times and profitability. A McKinsey report shows that in the industry it is common that:
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The data is highly available, but it is not systematically analyzed and the data sources of the tool groups are not linked.
The role of machine learning in providing the solution
The high degree of automation and advanced production equipment used in semiconductor manufacturing plants means that detailed production data over several years is potentially available. Using AI engines to connect quality control and yield data with process control data gives semiconductor manufacturers the foundation to identify yield losses, as well as identify their root causes.
This capability allows manufacturers to adjust production processes and chip designs to avoid problems, often using applications to monitor and adjust subprocesses in real time.
Specialized analytics companies are addressing the challenge facing semiconductor manufacturers and harnessing the power of data to improve yields:
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Qualicent Analytics, for example, builds AI engines that can determine optimal operating conditions for processes or products to significantly reduce manufacturing defects.
The results of using AI
The impact of using AI in the semiconductor industry has been enormous. Overall, the yield reduction has been reduced by up to 30%, and the cost reduction benefits of using AI-based algorithms for testing cover a variety of areas:
Conclusion
This case study provides an overview of infinite power of machine learning and what is possible for businesses. In the semiconductor industry alone, manufacturing yields have increased by up to 30%, scrap rates have been reduced, and manufacturing operations have been optimized.
However, this is just a glimpse of the enormous potential of machine learning to revolutionize the manufacturing industry. Among a multitude of other use cases, predictive maintenance of industrial equipment using AI is one of the fastest growing niches in the manufacturing sector.
Deloitte reports that AI-enhanced predictive maintenance can improve equipment availability with increases of approximately 10-20%, reduce time spent planning maintenance by up to 50%, and reduce equipment expenses by 10%. This would reduce overall maintenance costs by 10 percent; a potential savings of millions of dollars for large companies.
AI is already changing the way we do business, and its importance will grow in the years to come. The effects of machine learning will be far-reachingnot only in the semiconductor industry, but also in the transformation of the manufacturing industry as a whole. Businesses of all sizes need to be prepared. The adoption of Industry 4.0 will become increasingly essential as companies attempt to maintain their competitive edge and improve their bottom lines.