Demand for mission-critical semiconductor quality in broad-based consumer products is taxing leading IDMs and fabless companies, and the answer lies in leveraging existing manufacturing data.
For the semiconductor manufacturing market sector, the need to provide high-quality products has always been of paramount concern. However, in recent years, the quality demands on broad-market semiconductors used in many "smart" consumer electronics and high-end automobiles have begun to approach the levels of mission-critical ICs found in the medical and aerospace market sectors.
As these consumer-focused products become more content-heavy to enable a vast array of applications and capabilities, the semiconductor technology incorporated into them is being held to increasingly higher reliability and quality metrics.
For many leading semiconductor companies, they are meeting these intensifying quality challenges by efficiently and effectively leveraging the vast amounts of data that is generated during manufacturing test operations. While this data has existed for quite some time, the ability to benefit from it in very measureable ways has only recently become feasible and practical, creating a "ripple effect" across the entire spectrum of a semiconductor company's overall manufacturing and business operations.
By examining key aspects of manufacturing test operations, it is possible to see how improvement in specific areas can create a more far-reaching "ripple effect." For example, commercially-available technology now exists to implement superior outlier detection algorithms that can significantly improve the overall product quality in shipped parts. These improved results can be achieved by establishing quality indices that are based upon the results from multiple test operations such as e-test/WAT, wafer sort, final test, and system-level test.
Using production-proven simulation and analysis engines, semiconductor companies can implement real-time manufacturing test decisions based upon the monitoring and implementation of any combination of manufacturing test parameters. Once these indices have been established, the resulting detection algorithms can be deployed across the company's global supply chain operations. It is important to note that the foregoing improvements can be realized without disrupting or replacing existing manufacturing or test operations.
Beyond test operations, successfully collecting, analyzing, and leveraging manufacturing data can significantly improve other essential business operations including materials management, quality control, resource management, product design, and product delivery.
In today's business climate, where there is continual pressure to achieve greater manufacturing throughput while also improving quality, it is essential for semiconductor and electronics companies to examine their existing operations to see how they can be optimized to reach the next level of product quality and reliability.