Mr. Youngkwang Song Profile

Youngkwang Song

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Proceedings Article | 10 April 2024 Presentation + Paper

Device overlay root cause process detection using patterned wafer geometry information

Sejung Ham, Youngkwang Song, Cheolwoo Lee, Mingyu Kim, Minsung Hyun, Sunghyun Park, Moonsoup Kim, Jeonghoon Lee, Nanglyeom Oh, Sunglyul Park, Dongsub Choi

Proceedings Volume 12955, 129551D (2024) https://doi.org/10.1117/12.3008290

KEYWORDS: Semiconducting wafers, Overlay metrology, Optical alignment, Vacuum chambers, Lithography, Scanners, Optical parametric oscillators, Process control, Error analysis, 3D metrology

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On-product overlay (OPO) is a critical inline process control parameter in semiconductor manufacturing. One of the main factors that induce the overlay error is non-lithography processes like etching, deposition and cleaning. The overlay margin is getting tighter as the device technology advances and detecting the root cause of process-induced overlay error is a main problem to improving the OPO. However, it is not an easy problem to solve due to the lack of inline monitoring data on non-lithography processes. Even if we evaluate inline monitoring data, it is too sparse to do in-depth analysis compared to abundant lithographic overlay data. Instead, we can make use of data from the PWG patterned wafer geometry metrology system, which can measure high-density data with high throughput. In this paper, we introduce a comprehensive method of detecting the root cause of the process-induced overlay errors based on inline PWG data. Our target device is a 3D NAND product with process-induced overlay errors due to wafer geometry. We start our analysis by tracing PWG GEN3 data for the same wafer in a wide process step range. We compare the GEN3 signature to an overlay error signature of a target lithography layer to filter out suspicious processes. From the suspicious processes, we derived optimized KPIs that discriminate between good and bad wafers in terms of process-induced overlay errors, which are then used as a monitoring metric. With the optimized KPIs, we discern which process is the root cause of process-induced overlay errors to help drive corrective actions and improve OPO on the target device. Finally, we propose a comprehensive framework that is not limited to PWG data but applies to other available inline data such as alignment, ADI and AEI overlay and NZO.

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