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Semiconductor industry is moving towards miniature devices on the one end but on the other end the die sizes are becoming larger and larger. This coupled with highly capital intensive and competing environment of semiconductor manufacturing makes semiconductor test data management and yield improvement one of the most important facet of industry’s operations. Further, the semiconductor manufacturers have moved towards high volume production and shorter time to market with the additional KPI of achieving as little defects as possible. The semiconductor industry is moving towards de facto standard of defects per billion from defects per million.Operating and manufacturing of complex products at this scale requires collecting and analyzing large data sets and deducing trends and gaining insights about outliers that can hamper the smooth flow of manufacturing process.
View attachment 19664
The above premise suggests that semiconductor data analysis and yield improvement is becoming one of the most important tool in semiconductor manufacturing. Traditional methods of doing data analysis using Excel and other home grown tools are no longer feasible nor efficient at this scale. Most of these traditional tools are incapable of analyzing the huge amount of data that is generated in a modern semiconductor manufacturing unit. In order to overcome this incapability and the growing demand of reliability and quality of customers, advanced semiconductor data analysis software are now being widely used by semiconductor manufacturers to manage yield and to effectively monitor operations and maintain quality control metrics.
An end-to-end semiconductor yield analysis tool now includes the automated collection of data from multiple nodes of the operations, be it test floor or process equipment. The data is collected in real-time and analyzed by applying various advanced statistical methods for root cause analysis, process monitoring and yield enhancement. These preventive techniques lets the manufacturers achieve high volume production, reduced time to market and lower costs as a result of reduction in product defects.
View attachment 19665
Most of the end-to-end semiconductor yield analysis tools have incorporated dashboard modules in their offerings, these data dashboards gives instant insight into the whole manufacturing process. These dashboard provides a variety of charts and analytics and can be customized to cater to various management levels. The test and product engineers can have advanced analytics covering parametric data, trends while the higher management can have the detailed analytics covering numbers of devices tested or the number of products shipped in the last quarter, a trend on quarter to quarter basis and so on that impact business decisions. These highly customized intuitive dashboards have made semiconductor yield management manageable in the era where production volumes have scaled and complexity has immensely increased.
Semiconductor data analysis software is not a new concept, they have been around for a while now and the semiconductor manufacturers have long praised their efficacy but the icing on top is that these advanced semiconductor test data and yield management tools have gone mobile. The engineers and top management can view these insights on their mobile phone and tablets on the go, they can export the charts and see alerts if there are any anomalies. Thus, giving them a tool that can help them manage high volume productions without impacting their operational efficiency and maintaining quality assurance.
View attachment 19664
The above premise suggests that semiconductor data analysis and yield improvement is becoming one of the most important tool in semiconductor manufacturing. Traditional methods of doing data analysis using Excel and other home grown tools are no longer feasible nor efficient at this scale. Most of these traditional tools are incapable of analyzing the huge amount of data that is generated in a modern semiconductor manufacturing unit. In order to overcome this incapability and the growing demand of reliability and quality of customers, advanced semiconductor data analysis software are now being widely used by semiconductor manufacturers to manage yield and to effectively monitor operations and maintain quality control metrics.
An end-to-end semiconductor yield analysis tool now includes the automated collection of data from multiple nodes of the operations, be it test floor or process equipment. The data is collected in real-time and analyzed by applying various advanced statistical methods for root cause analysis, process monitoring and yield enhancement. These preventive techniques lets the manufacturers achieve high volume production, reduced time to market and lower costs as a result of reduction in product defects.
View attachment 19665
Most of the end-to-end semiconductor yield analysis tools have incorporated dashboard modules in their offerings, these data dashboards gives instant insight into the whole manufacturing process. These dashboard provides a variety of charts and analytics and can be customized to cater to various management levels. The test and product engineers can have advanced analytics covering parametric data, trends while the higher management can have the detailed analytics covering numbers of devices tested or the number of products shipped in the last quarter, a trend on quarter to quarter basis and so on that impact business decisions. These highly customized intuitive dashboards have made semiconductor yield management manageable in the era where production volumes have scaled and complexity has immensely increased.
Semiconductor data analysis software is not a new concept, they have been around for a while now and the semiconductor manufacturers have long praised their efficacy but the icing on top is that these advanced semiconductor test data and yield management tools have gone mobile. The engineers and top management can view these insights on their mobile phone and tablets on the go, they can export the charts and see alerts if there are any anomalies. Thus, giving them a tool that can help them manage high volume productions without impacting their operational efficiency and maintaining quality assurance.
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