Massive metrology and failure identification for DRAM applications (Conference Presentation)

Harm Dillen; Dorothe Oorschot; Marleen Kooiman; Willem van Mierlo; Ziyang Wang; Kang-San Lee; Jin-Woo Lee; Ruochong Fei; Shu-Yu Lai; Marc Kea; Inhwan Lee; Hwan Kim; Junghyun Kang; Jaehee Hwang; Chang-Moon Lim

doi:10.1117/12.2515487

26 March 2019 Massive metrology and failure identification for DRAM applications (Conference Presentation)

Harm Dillen, Dorothe Oorschot, Marleen Kooiman, Willem van Mierlo, Ziyang Wang, Kang-San Lee, Jin-Woo Lee, Ruochong Fei, Shu-Yu Lai, Marc Kea, Inhwan Lee, Hwan Kim, Junghyun Kang, Jaehee Hwang, Chang-Moon Lim

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Proceedings Volume 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII; 109591K (2019) https://doi.org/10.1117/12.2515487
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

Abstract

Introduction and problem statement Given that EUV lithography allows printing smaller Critical Dimension (CD) features, it can result in non-normal distributed CD populations on ADI wafers [Civay SPIE AL 2014], leading to errors in predicted failure rates [Bristol SPIE AL 2017]. As a result, there is a need to quantify the actual behavior of the CD population extremes by means of massive metrology [Dillen EUVL 2018]. Not only allows this to study the CD distribution, we can in parallel also evaluate pattern quality and the failure mechanisms leading to defects. This massive metrology method provides an accurate failure rate based on CD, and enables new possibilities to define a failure rate based on different metrics in a single measurement. Method We analyze the CD uniformity of pillars in polar coordinates using a global waveform based thresholding strategy. In conjunction with this CD information, we also evaluated the print quality of each individual measured feature. Fig 1. In line detected anomalies and failure definitions As we gather this information during the measurement of CD, we can limit the additional measurement overhead to neglectable levels. Application and outlook We will show how we can leverage this to determine a defect based process window and relations of failure mechanisms through process conditions (see figure 2). When we take failures in a CH dataset into account, we illustrate the effect on the shape of a large dataset distribution in figure 3. Fig 2. Defect identification for a through exposure dose experiment of pillars. For each condition >13k pillars where measured. The plot clearly shows an asymmetric behavior due to different failure mechanisms at low and high energy. The 2 vertical lines at relative energies 0.93 and 1.05 times nominal indicate the low defect process window. Fig 3. A distribution of measured regular grid dense CH. The red line is the unfiltered CD data, the blue line is the shape of the distribution after filtering individual CH measurements that have a much lower contrast than expected.

Conference Presentation

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Harm Dillen, Dorothe Oorschot, Marleen Kooiman, Willem van Mierlo, Ziyang Wang, Kang-San Lee, Jin-Woo Lee, Ruochong Fei, Shu-Yu Lai, Marc Kea, Inhwan Lee, Hwan Kim, Junghyun Kang, Jaehee Hwang, and Chang-Moon Lim "Massive metrology and failure identification for DRAM applications (Conference Presentation)", Proc. SPIE 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII, 109591K (26 March 2019); https://doi.org/10.1117/12.2515487

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KEYWORDS

Failure analysis

Critical dimension metrology

Metrology

Extreme ultraviolet lithography

Printing

Semiconducting wafers

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