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Unlocking the Promise of AI in Electronic Manufacturing
October 29, 2025 | Shobhit Agrawal, Keysight TechnologiesEstimated reading time: 1 minute
(Editor’s note: This paper was originally published in the IPC APEX EXPO 2025 Technical Conference proceedings.)
The electronics manufacturing industry is rapidly evolving as more complicated products are introduced in the production lines, which require technological advancements even in the production processes. The requirements for production that is efficient, product quality that is greater, and product life cycles that are shorter are more crucial than ever before. In the electronic device life cycle, from design to maintenance, test phases have a significant impact on the economy of the company. Test processes are closely linked to the production volume and impacted by the complexity of the product. For businesses to maintain their competitive edge, they need to adopt innovative solutions and redefine processes. AI is one such solution that is being widely adopted. The historical data collected during the lifecycle and test phases is analyzed to fine-tune equipment, identify the most efficient production processes, and reduce wastage.
Most of manufacturing faces challenges which are common across organizations and can be easily solved using AI. As companies are getting more data-driven, they are setting up processes and systems to capture structured as well as unstructured data. This data can be fed to AI engines to help engineers in troubleshooting new issues in highly complex production lines, to find the machine performance data, most relevant procedures, and history of relevant issues. Companies that are adopting AI can quickly identify the root cause of different issues, solve them, and integrate those learnings to optimize processes.
AI transformation has just begun and still is in early stages of development. It is expected to rapidly grow and disrupt traditional problem-solving methods currently followed in manufacturing. AI use cases have demonstrated tangible value and ability to be implemented at scale.
To continue reading this article, which originally appeared in the October 2025 edition of SMT007 Magazine, click here.
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