Internet of Things (IoT) is more than a buzz word now; with the focus of almost all the big companies like Intel and Cisco, millions of devices are connected to each other now. These devices are performing various tasks from as simple as recording values through temperatures sensors to devices triggering critical actions in strategic and remote locations. On one hand, the IoT boom has led to increased production of semiconductor devices, but on the other hand, it has increased the customers’ focus on the quality. The task-critical nature of these devices has made it compulsory to have stricter quality-control metrics that leads to higher return-materials authorization (RMA), resulting in overall lower yield. One systematic way to manage yield and reduce RMA is through the use of an end-to-end semiconductor yield analysis tool.
The semiconductor manufacturers are moving from defects per million to defects per billion, and in order to achieve it, companies are collecting data at every node of the manufacturing process and performing numerous tests, making it costly as well as delaying the product ramp-ups and increasing time to market. The production delivery gets further affected when there are number of RMAs. The cost of RMA is not just the material wastage cost or the machinery overrun but also the effect on the reputation of the company. Yield-management software or Semiconductor data-analysis software comes in really handy in these cases; such software is able to collect data from test sites as well as the operation floor, map the data to a standardized format, and then perform complex analysis to find the root-cause of failures and defects. The yield management in semiconductor manufacturing these day is not just about improving the wafer yield—rather it focuses on operational intelligence, connecting the data across various nodes of the supply chain and coming up with predictive models to reduce RMAs and to improve the overall yield of the manufacturing process.
As the number of devices being interconnected is increasing, the complexity of these devices and the functions they perform is becoming critical. The dependency of humans as well as business operations on these interconnected devices is increasing, making the world heavily reliant on technology and IoT based platforms. The room for error is already very little, and it reduces even more when these semiconductor devices are deployed in strategic and remote locations. Owing to this very reason, the engineers and purchasing authorities have made stricter criteria for procurement-related decision-making—the biggest concerns being the security of data, energy requirements, and provision of energy for the semiconductor devices.
Yield-management software for semiconductor industry is moving towards big data and IoT analytics as the volume, variety, and velocity of the data being generated, collected, and analyzed have transformed. This calls for specialized analyses performed by complex tools, which are more than mere STDF data-analysis software tools. The new tools are able to pinpoint issues with the test equipment and test programs as well as operational issues on test floor in real-time.
Some of these RMAs are attributed to material failures while a good percentage of these failures are because of false flags raised by tester, which may have malfunctioned or because the test program may not be suitable for that particular scenario. The process of incorporating these metrics in identifying false RMAs as well as streamlining operations have resulted in cost savings due to reduced RMAs as well as faster ramp-ups and product deliveries. Because of this specialized and added analytical layer, the semiconductor manufacturers are more confident about the product quality and performance, giving them higher return on investment, reduced wastage cost, and increased business reputation, directly impacting their revenues and bottom-line.
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