Improving back end of line productivity through smart automation

摘要

Despite recently receiving a large amount of global publicity, smart automation is yet to be fully implemented in production for many areas, including mask making for semiconductors. One specific area that can significantly benefit from smart automation is the back end of line (BEOL) in mask manufacturing where the implementation of data driven decision making and predictive analytics can completely revolutionize our current way of working. Apart from any hardware aspect, software must adapt to the current needs of connectivity which demand the ability to handle large amounts of data, have sufficient computational resources and execute tool-to-tool communication. These requirements call for flexible and expandable software applications that increase the productivity and efficiency of backend processes. Additionally, by incorporating automated systems, businesses benefit from the reduction or elimination of losses due to human error. Given the number of human interactions within each step of the standard BEOL, such as inspection, cleaning, disposition/review and repair, mask shops run a high risk of a mishap occurring. Even by extensive measures such errors can only be reduced but not completely avoided as their origin lies in the way of how humans act. The consequences can range from harmless slip-ups up to severe manufacturing impacts which finally can lead to an economic loss. These risk levels become further multiplied as both product and workflow become more complex due to the possible repetitive cycles in the repair steps. These losses can be mitigated by the use of smart automated solutions that deliver a reduction in turnaround time (TAT) and overhead. More efficient use of operator expertise and cost reductions in data handling will improve mask shops' productivity. Another issue that intelligent automation brings is efficient tool management. In a high volume manufacturing environment it can be challenging to maintain active monitoring of tools. Consequently, idle times and bottlenecks prevent mask shops from achieving their highest potential in terms of cycle time and reliability in delivering products on time. Having the possibility to monitor the tool clusters enables efficient delegation of operations and facilitates the optimization of workflows. The proposed model in this paper investigates the effects of defectivity complexity on the TAT in a mask shop. The inclusion of intelligent application solutions effectively address human error, bottlenecks and defect complexity reducing both TAT and TAT variability. Smart automation coupled with real time monitoring and decision making solutions help control the BEOL in a predictive manner. Therefore optimization of the BEOL workflow through intelligent automation leads to a mask production with higher reliability and higher market value.