SEM AutoAnalysis for reduced turnaround time and to ensure repair quality of EUV photomasks

摘要

With the semiconductor industry tending towards adding multiple layers consisting of EUV technology in high-endmanufacturing and the production of many EUV scanners to meet customer demands, novel approaches for EUV defectreview are being readily investigated. The successor of the quasi industry standard AIMS? and sole actinic defectreview tool available currently is AIMS? EUV. As the industry already introduced this newcomer in the manufacturingenvironment, other steps in the workflow were forced to adapt to the new technology. One example is the automatedaerial image analysis process where the DUV aerial image analysis software, AIMS? AutoAnalysis (AAA), wasenhanced for the EUV solution in order to handle high resolution EUV images. This was a necessary step for fullautomation similar to the process achieved with AIMS? and AAA.Another important domain in the back end of line is defect repair where the e-beam based repair tool MeRiT? is also thequasi standard in the mask manufacturing industry especially for high-end photomasks. After undergoing changes tokeep up with shrinking feature sizes and complex repairs MeRiT? tools were able to overcome these challenges andfulfill the current industry demands and expectations. For mask makers timely supply of error free high-quality masks isof the essence which can be further ensured by introducing a higher level of automation to the repair workflow.Following a similar approach to the optical counterpart, a digital solution known as SEM AutoAnalysis (SAA) has beendeveloped. With SAA, a quick and fully automated SEM image-based quality assessment after a repair of a photomask isreadily achievable. Moreover, the repair technicians benefit vastly by having the complete repair history of a defect fortheir decision-making process which would lead to a reduction of the turnaround time. As a consequence, unnecessarytime wastes during mask un/loading cycles can be avoided.The myriad data produced in the BEOL, originating from different modalities, can be converted to meaningfulinformation with the help of automation enabling technicians to make better decisions, reducing the risk of mishaps,improve repair quality and reliability of processes in general. Since mask defects that go through each tool are the same,data produced by different tools should retain that common denominator for an efficient assessment. This assessmentneeds to be applied to the areas of different modalities where a comparison is possible that led to the investigations totest the feasibility of combining SEM and EUV data. A comparison of SAA results with AIMS? EUV measurementsanalyzed with AAA on the same photomask and defects are presented along with this proceeding. The results show thatSAA can provide a valuable preliminary assessment of photomask repairs. Nevertheless, due to the nature of SEM basedanalysis, AIMS? EUV technology remains mandatory for a final mask repair qualification and a complete specificationcheck, i.e. mask repair verification. The outcome of this investigation paves the way towards a fully automated BEOLwhere different workflows and data originating from several tools in the mask shop can be interconnected and controlled.