In order to optimize yield in DRAM semiconductor manufacturing for 2x nodes and beyond, the (processing induced) overlay fingerprint towards the edge of the wafer needs to be reduced. Traditionally, this is achieved by acquiring denser overlay metrology at the edge of the wafer, to feed field-by-field corrections. Although field-by-field corrections can be effective in reducing localized overlay errors, the requirement for dense metrology to determine the corrections can become a limiting factor due to a significant increase of metrology time and cost. In this study, a more cost-effective solution has been found in extending the regular correction model with an edge-specific component. This new overlay correction model can be driven by an optimized, sparser sampling especially at the wafer edge area, and also allows for a reduction of noise propagation. Lithography correction potential has been maximized, with significantly less metrology needs. Evaluations have been performed, demonstrating the benefit of edge models in terms of on-product overlay performance, as well as cell based overlay performance based on metrology-to-cell matching improvements. Performance can be increased compared to POR modeling and sampling, which can contribute to (overlay based) yield improvement. Based on advanced modeling including edge components, metrology requirements have been optimized, enabling integrated metrology which drives down overall metrology fab footprint and lithography cycle time.
As DRAM semiconductor manufacturing approaches high volume for 1x nm nodes with immersion lithography, an increased emphasis is being placed on reducing the influence of the systematic wafer-level contribution to the on-product overlay budget. The cost of the needed metrology has hitherto been challenging. However, it will be shown that the availability of fast, accurate diffraction based metrology integrated within the Lithography cluster can enable cost-effective solutions. Together with applications software we will use any relevant context information to optimize control of all exposure-tool actuators during lot processing, to deliver the needed on-product performance.
Current process corrections typically are done based on feedback per lot and per exposure chuck. Wafers exposed on the same chuck, belonging to the same lot get exactly the same process corrections. In current HVM processing however, an important contribution to the wafer variation is the differences in processing of the individual wafers. These differences can be related to variations in the usage of the processing tools (e.g. different etch chambers). An extension of the process corrections from chuck-based to process-context based can help in reducing the systematic wafer-level variation. With Integrated Metrology the sampling of wafers through the lot can be adjusted to make sure all different processing-contexts are covered in the measurements.
Finally, the impact on Litho process cycle time of the total metrology effort required to enable these performance improvements, will be evaluated, and a proposal will be made on the optimum strategy to enable high-volume manufacturing.
In order to handle the upcoming 1x DRAM overlay and yield requirements, metrology needs to evolve to more accurately represent product device patterns while being robust to process effects. One way to address this is to optimize the metrology target design. A viable solution needs to address multiple challenges. The target needs to be resistant to process damage. A single target needs to measure overlay between two or more layers. Targets need to meet design rule and depth of focus requirements under extreme illumination conditions. These must be achieved while maintaining good precision and throughput with an ultra-small target. In this publication, a holistic approach is used to address these challenges, using computationally optimized metrology targets with an advanced overlay control loop.
While semiconductor manufacturing moves toward the 7nm node for logic and 15nm node for memory, an increased emphasis has been placed on reducing the influence known contributors have toward the on product overlay budget. With a machine learning technique known as function approximation, we use a neural network to gain insight to how known contributors, such as those collected with scanner metrology, influence the on product overlay budget. The result is a sufficiently trained function that can approximate overlay for all wafers exposed with the lithography system. As a real world application, inline metrology can be used to measure overlay for a few wafers while using the trained function to approximate overlay vector maps for the entire lot of wafers. With the approximated overlay vector maps for all wafers coming off the track, a process engineer can redirect wafers or lots with overlay signatures outside the standard population to offline metrology for excursion validation. With this added flexibility, engineers will be given more opportunities to catch wafers that need to be reworked, resulting in improved yield. The quality of the derived corrections from measured overlay metrology feedback can be improved using the approximated overlay to trigger, which wafers should or shouldn’t be, measured inline. As a development or integration engineer the approximated overlay can be used to gain insight into lots and wafers used for design of experiments (DOE) troubleshooting. In this paper we will present the results of a case study that follows the machine learning function approximation approach to data analysis, with production overlay measured on an inline metrology system at SK hynix.
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