The thickness standard deviation (sigma) of SiO2 thin layer on the silicon wafer is high
The high standard deviation of the SiO2 thin layer on the silicon wafer is bad because it indicates that the layer is not uniform and that there are significant variations in the thickness across the wafer. This can lead to issues with the performance of the device and potential reliability issues.
Use Process Functional Modeling (PFM) to create a model of the process and a model of the failure. Generate a solution
We do not understand the reason for the different processes in the different zones
The process conditions are different in the different zones of the furnace. Wafers in the low zone show worse performance.
Wet cleaning is widely used in microchip manufacturing. Single wafer equipment is working as follows. A wafer rotates, and chemistry is poured from a movable nozzle. Water rinsing is performed at the end of the process. Loading of a new batch of the chemistry resulted in excursion - a strongly increased amount of defects was observed on the wafer after the processing. The project is dedicated to the failure analysis and creation of innovative solutions.
The project was dedicated to production yield improvement in microchip manufacturing. The bumps are created on the top of a wafer and used for the final test of all dies. Only good dies are taken for the packaging. All dies that fail the test will be scrapped. The process yield depends on the amount of "good" and "bad" dies. It was revealed that in some cases, the time between the end of the process and the final test impacts the yield. The longer the dwelling, the more dies fail the final test. If the dwelling exceeds hundreds of hours, the amount of failed dies becomes dramatically high, which results in the scrapping of the whole wafer. The problem was analyzed and solved.
The number of particles is a critical parameter for microchip manufacturing. Each, even a very small particle, can potentially destroy a die. Therefore filters are widely used. Water is always filtered through fine filters to reduce the number of particles. Nevertheless, if the filter is too fine, it could cause a problem. This issue was investigated with the help of Functional Modeling. Possible solutions were generated using 40 Inventive Principles.