Mr. Hyunjoo Lee Profile

Hyunjoo Lee

Photomask Principal Engineer at SAMSUNG Electronics Co Ltd

SPIE Involvement:

Author

Photomask E-beam, Laser writer manager
Photomask Cleaning senior manager
Photomask Repair senior manager
Photomask Bake,Dev senior manager
Photomask Dry etch senior manager
Photomask Technical Innovation Leader

Proceedings Article | 1 December 2022 Paper

Introduction of new visual analysis program using defect image segmentation for EUV mask mass product at modern load port

Hyunjoo Lee, Kyunghyun Kim, Yeongjun Shim, Jinsung Choi, Jaewhan Sung, Sanghee Lee

Proceedings Volume 12293, 1229303 (2022) https://doi.org/10.1117/12.2641750

KEYWORDS: Image processing, Photomasks, Inspection, Extreme ultraviolet, Coating, Image segmentation, Image quality, Etching, Sensors, Optical inspection

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The EUV (Extreme Ultraviolet) lithography is certainly technology for 10nm or less which was used to mass-produce chips contributes to improving the minimum feature size, reducing the process step by enabling DPT (Double Patterning Technology)-less, and improving Fab. operation. Due to the expansion of EUV layers in mass production of below 5nm, EUV mask layers are continuously increasing for mass product. In order to EUV mask mass product, it is important to investment of very high expensive EUV tools and facilities, improve longterm TAT (Turn Around Time), and management of challenge yields. Among the manager of stable yields which are performed just after every process to minimize the continuous defect of mask during the process handling, take an inspection every single mask is the best way. However, investment in inspection tools and increase in inspection step are not efficient due to inefficient factory operation due to very high cost and long TAT delay. Currently most mask manufacturing companies are using manual visual inspection by human eyes and microscope. In this paper, quality monitoring system was developed to detect micro-unit front and back defects, scratches, contaminations, and coating defects by applying the Image Segmentation technique to photos taken on the front and back of the mask by modern load port (refer to wafer EFEM: End Front Equipment Module). In an effort to understand further, the authors evaluated three image segmentations technologies using CNN (Convolutional Neural Network), Sobel Edge Detector, AI (Artificial Intelligence) for mask yield managing program. This method can provide the means for determining scraps and analyzing complex log files for a quality issue found in mask fabrication. These challenges will make a paradigm shift in mask industry for the EUV mask mass product to make chips. The mask tool manufacturers unify load port specifications, it will be able to contribute to new technology and process improvement in the future.

Proceedings Article | 12 October 2021 Presentation + Paper

Study for big data interrelation using real-time monitoring technology in high cost, high volume manufacturing EUV era

Hyun-Joo Lee, Euihyun Jung, Daein Kim, Byoungsu Kim, Seun Yang, Jaewhan Sung, Joonsoo Park

Proceedings Volume 11855, 118550A (2021) https://doi.org/10.1117/12.2599020

KEYWORDS: Particles, Inspection, Photomasks, Digital image processing, Image processing, Sensors, Extreme ultraviolet, Process control, Manufacturing, Plasma

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A sampling inspection using non-patterned wafer and photomask has been dedicated to classical inspection technology for monitoring trend of particle variation at mass production. Total cost of sampling inspection method decreases the overall equipment effectiveness in mass production because equipment usage time and raw material cost. Nevertheless, customer mass production yield requirements for high-grade photomask and extreme ultraviolet photomask by sampling inspection method will be difficult to satisfy. To overcome sampling inspection's low reliability, this paper intended to describe an application of real-time monitoring for mass production equipment and verification of evaluated case by case. Optimization of real-time monitoring setup requires that sensor's install location with a considered mean free path in vacuum chamber, avoid to particle and bubble in chemical tube line and filter, and digital image process comparing method for nozzle height and parts location. An emergence of many by-products in a vacuum chamber, chemical tube line, and chemical filter is an unexpected danger. Application of real-time monitoring contributes to observing particles that in vacuum chamber using plasma, in tube line using chemical and chemical filters, sensing of mechanical drift and twist are also applicable with real-time detection technology using high-resolution cameras. As mentioned above, saved real-time big data can use proactive control to improve yield loss and cost of ownership. The specific suggestion about using real-time monitoring method is as follows 1. Detected increase rapidly trend of particle. Stop a process and start a particle removing recipe. 2. Observed particle rising. Stop process and start a cure recipe, and change other best path. 3. Sensed abnormal action. Stop process and do preventive maintenance after all substrate out. Both real-time monitoring data and yield data can analyze correlations that improve to become low cost of ownership by figuring out a root cause and drop in quality. A photomask industry is small compare to semiconductor industries, less than 1 percent by number of tools and production capacity. A photomask industry hard to make a big data due to little seed or small volume data. This paper shows how to make big data using real-time monitoring technology and how to defend a yield loss by unexpected situation at photomask tools.

Proceedings Article | 8 November 2012 Paper

Study for compensation of unexpected image placement error caused by VSB mask writer deflector

Hyun-joo Lee, Min-kyu Choi, Seong-yong Moon, Han-Ku Cho, Jonggul Doh, Jinho Ahn

Proceedings Volume 8522, 852228 (2012) https://doi.org/10.1117/12.964098

KEYWORDS: Vestigial sideband modulation, Photomasks, Associative arrays, Contamination, Electron beams, Beam shaping, Reliability, Calibration, Data modeling, Semiconductors

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Proceedings Article | 23 February 2012 Paper

Si-based optical I/O for optical memory interface

Kyoungho Ha, Dongjae Shin, Hyunil Byun, Kwansik Cho, Kyoungwon Na, Hochul Ji, Junghyung Pyo, Seokyong Hong, Kwanghyun Lee, Beomseok Lee, Yong-hwack Shin, Junghye Kim, Seong-gu Kim, Insung Joe, Sungdong Suh, Sanghoon Choi, Sangdeok Han, Yoondong Park, Hanmei Choi, Bongjin Kuh, Kichul Kim, Jinwoo Choi, Sujin Park, Hyeunsu Kim, Kiho Kim, Jinyong Choi, Hyunjoo Lee, Sujin Yang, Sungho Park, Minwoo Lee, Minchang Cho, Saebyeol Kim, Taejin Jeong, Seokhun Hyun, Cheongryong Cho, Jeong-kyoum Kim, Hong-gu Yoon, Jeongsik Nam, Hyukjoon Kwon, Hocheol Lee, Junghwan Choi, Sungjin Jang, Joosun Choi, Chilhee Chung

Proceedings Volume 8267, 82670F (2012) https://doi.org/10.1117/12.912794

KEYWORDS: Interfaces, Modulators, Waveguides, Photodiodes, Silicon, Optical components, Oxides, Optical interconnects, Eye, Crystals

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