Advanced analytics capability can contribute to semiconductor manufacturing as well as proactive estimation of equipment failures.
FREMONT, CA: Advanced analytics solutions are increasingly being deployed by companies to gain operational efficiency and productivity. The semiconductor industry has also realized the potential of analytics and is using it for semiconductor manufacturing, R&D, sales as well as enhanced pricing capabilities. At present, the semiconductor industry is ripe for advanced analytics incorporation.
In the case of chip manufacturing, the volume of data collected on the fabrication floor has undergone constant expansion. The state-of-the-art edge tools have numerous measuring instruments, and each one of them collects over 300 sensor inputs. Thus, the total information collected, including metrics for products, processes, and machine state, will easily exceed terabytes of data. Thus, an advanced analytics solution will be extremely useful in the case of various manufacturing dimensions, such as throughput, yield, operating costs, and equipment availability.
Analytics can also aid to decrease equipment downtime. The fab can carry out multivariate analysis to improve condition-based monitoring, which is a maintenance strategy involving examination of indicators to check if equipment performance is decreasing. Such an analysis will also help the fab to predict precisely when consumables or parts will fail. The above information will enable the fab to optimize the planned maintenance efforts reducing downtime as well as an expense on labor and parts.
Apart from preventing equipment failures, fab can leverage advanced analytics for more sophisticated purposes as well. For instance, they can link process-level and equipment data to metrology and inspection data to make more precise predictions over yield degradation and yield failure. Thus, advanced analytics can aid in predicting process failure by identifying patterns in the data before actually building any model.
Semiconductor manufacturers are increasingly craving for improved analytics capabilities to address manufacturing and equipment downtime issues.
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