Miguel Alonso Merino

Knowledge-based software system for fast yield loss detection in a semiconductor fab

The comparative analysis of process machines in terms of yield related metrics (such as probe and E-Test data, process and particle data,. ..) is a source of a great deal of information for yield improvement. With this aim we published on SPIEs Microelectronic Manufacturing an Advanced Software System to detect machine-related yield limitors using a comparative analysis. This paper presents the natural expansion of that Software System by converting it into a more knowledge-based tool for fast yield loss detection on a semiconductor fab. The new System performs, in an automatic mode, the comparison among machines for every single step selected in the fabrication routing. The detection of statistically significative differences among machines at every step is performed using algorithms that incorporate the overall analysts experience on our fab. The output of the System allows a fast detection and reaction to yield issues, mainly to those that are still on the initial or baseline stages.

The 100% yield explanation approach in Lucent Technologies Madrid

A wealth of advantages arise from breaking down the overall yield into yield components that are easier to work and closer to the manufacturing line environment. We present in this paper our strategy to attempt the 100% yield explanation on our fab and the process of building a pareto that quantifies the impact of each yield component (defects, probe, nothing found, etc...). The most critical one, the defect related, is accounted by a set of knowledge-based automatic software tools that operate in our fab. They quantify it and break it down into the by layer, by defect size and type (ADC) contributions. The step forward of communication and deployment of this yield strategy is a key topic also discussed in the paper. On our way towards the 100% understanding of yield we have learned how to better manage it and taken advantage of many more opportunities to improve it. The strategy has shown to work both for new and mature technologies in our manufacturing line in Lucent Technologies Madrid.

Costs of Type 1 Diabetes Mellitus in Pediatric Patients in Spain: Chrystal Observational Study

Julio López-Bastida,1,2 Juan Oliva,1,3 Juan Pedro López-Siguero,4 Luis Alberto Vázquez,5 Dingfeng Jiang,6 Renata Villoro,1 Isaac Aranda,1,3 María Merino,1 Tatiana Dilla,5 Jesús Reviriego,5 Magaly Perez-Nieves6 1 Instituto Max Weber, Madrid, Spain; 2 Nursing and Occupational and Speech Therapy Department. Universidad de Castilla-La Mancha, Spain; 3 Economics Department. Universidad de Castilla-La Mancha, Spain; 4 Pediatric Endocrinology Department, Hospital Universitario Carlos Haya, Málaga, Spain; 5 Eli Lilly and Company, Madrid, Spain; 6 Eli Lilly and Company, Indianapolis, IN, USA.

Wafer level stress data successfully used as early burn-in predictor

We show that a simple post-stress test can provide a good early reliability indicator. The defect types that have been revealed by this post-stress test are two types of conductive particles on metal levels. This early indicator has been of great value when dealing with potentially contaminated wafers/lots and to evaluate and to prioritize the corrective actions to solve the line issues.

Achieving good correlation results between bitmap and TENCOR data

Software to correlate memory bitmap to defect data has become a standard tool for yield analysis, because it provides extraordinarily useful information about yield limiters. Currently, this analysis software is limited to process KLA data. Although TENCOR inspection machines are widely used as production inspection tools, defect information provided by the TENCORs has not been used to correlate to bitmap data, because the TENCOR alignment capabilities are not good enough to achieve good correlation results. We present a method to correlate bitmap information to TENCOR data. This method is based on a set of algorithms used to process the TENCOR inspection files to minimize the native misalignment problems present in the TENCOR inspection machines. By means of these algorithms, TENCOR data can be correlated to the bitmap data as well as that of KLAs, opening new possibilities for the yield analyst.

Wafer-level stress data successfully used as early burn-in predictor

We show that a simple post-stress test can provide a good early reliability indicator. The defect types that have been revealed by this post-stress test are two types of conductive particles on metal levels. This early indicator has been of great value when dealing with potentially contaminated wafers/lots and to evaluate and to prioritize the corrective actions to solve the line issues.

Toward 100% yield understanding approach in Lucent Technologies Madrid

A wealth of advantages arise form breaking down the overall yield into yield components that are easier to work and closer to the manufacturing line environment. We present in this paper our strategy to attempt the 100 percent yield explanation on our fab and the process of building a pareto that quantifies the impact of each yield component. The most critical one, the defect related, is accounted by a set of knowledge-based automatic software tools that operate in our fab. They quantify it and break it down into the by layer, by defect size and type contributions. The step forward of communication and deployment of this yield strategy is a key topic also discussed in the paper. On our way towards the 100 percent understanding of yield we have learned how to better manage it and taken advantage of many more opportunities to improve it. The strategy has shown to work both for new and mature technologies in our manufacturing line in Lucent Technologies Madrid.