Delivering high-quality electronic devices that are able to perform in remote locations and drastic conditions has become a competitive advantage for the new age semiconductor manufacturers that are trying to ride the IoT wave and want to be the early members of the bandwagon. This has resulted in improved focus of quality, and the manufacturers are gearing towards reducing the defect rate from defects per million (DPM) to defects per billion (DPB).
This change of focus is primarily attributed to the demand for high-quality products by the end customers and the ever increasing competition in the semiconductor manufacturing industry. One important aspect that is used directly to monitor the quality and reliability of semiconductor equipment is by performing semiconductor engineering data analysis at each node of the operations. A huge amount of data is generated and collected during the manufacturing process; this data is further analyzed to monitor the operations and for overall yield improvement in semiconductor manufacturing.
Product engineers and technical managers have understood the importance of analyzing engineering data for the management of yield and the reduction of faults in semiconductor manufacturing. The ability to turn raw data into actionable intelligence results in capital productivity that in turn generates competitive advantage for the semiconductor manufacturers, thereby identifying an undetected cause of faults and curbing them effectively at initial stages, saving the company from losses in financial terms as well as reputation-wise. The analyzed data coupled with effective strategies can lead to significant yield improvements, cost reductions and an accelerated time to market, giving the semiconductor manufacturers advantage in product development cycle and eventually supporting the bottom-line of the company.
Semiconductor manufacturing yield-management tools are used to analyze various patterns generated in the process data; this data is collected during various stages of manufacturing process. Semiconductor manufacturing is a complex process, and it requires significant expertise to analyze and perform actions in case any anomalies are detected. The advanced and sophisticated, yet simple-to-use, data-analysis and yield-management tools help the product and test engineers analyze and locate the source of anomalies at a very early stage. The analysis runs on the basic building block of semiconductor devices—semiconductor wafers. The material or processing defects encountered at the wafer-level impacts the overall performance of the semiconductor device and results in yield loss as well, causing a bad reputation and customer dissatisfaction, while adding to the increased cost attributed to RMAs and yield losses to the semiconductor manufacturer.
The importance and efficacy of modern and advanced data-analysis technologies can also be gauged with the increase in the learning rate of the organization on yield- and wafer-related issues. This post-silicon data analysis can greatly help the engineers at the semiconductor manufacturing units in identifying root-cause issues that can be easily nipped in the bud. In a case where the engineers fail to cure these issues, and if the wafers become successful in moving forward to the production cycle, the resulting wafers will greatly affect the economics as they have to be either discarded in the later stage (final test) or once returned by the disgruntled customers, resulting in lower yield and causing delay in product ramp-up.
It is imperative to understand the importance of data engineering in the successful operation and management of semiconductor manufacturing and the economics associated with it. As semiconductor manufacturers invest large sums of capital in setting up the assembly and process equipment for manufacturing and shipping quality products, they also want their investments to generate good returns for them and that too in the quickest possible timeframe. Hence, rapid learning about the operations and optimal process designs via analysis of engineering data is of utmost importance to them. It is also a valuable source of competitive advantage for the semiconductor manufacturers, and without the help of proper data analysis and yield management tools, they won’t be able to achieve their operational and business goals.
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