• Title/Summary/Keyword: 화학적 기계적 연마장치

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The effects of polishing technique and brushing on the surface roughness of acrylic resin (연마 방법과 칫솔질이 아크릴릭 레진의 표면 거칠기에 미치는 영향)

  • Lee, Ju-Ri;Jeong, Cheol-Ho;Choi, Jung-Han;Hwang, Jae-Woong;Lee, Dong-Hwan
    • The Journal of Korean Academy of Prosthodontics
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    • v.48 no.4
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    • pp.287-293
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    • 2010
  • Purpose: This study evaluated the effect of polishing techniques on surface roughness of polymethyl methacrylate (PMMA), as well as the influence of light-cured surface glaze and subsequent brushing on surface roughness. Materials and methods: A total of 60 PMMA specimens ($10{\times}10{\times}5\;mm$) were made and then divided into 6 groups of 10 each according to the polymerization methods (under pressure or atmosphere) and the surface polishing methods (mechanical or chemical polishing) including 2 control groups. The mechanical polishing was performed with the carbide denture bur, rubber points and then pumice and lathe wheel. The chemical polishing was performed by applying a light-cured surface glaze ($Plaquit^{(R)}$; Dreve-Dentamid GmbH). Accura $2000^{(R)}$, a non-contact, non-destructive, optical 3-dimensional surface analysis system, was used to measure the surface roughness (Ra) and 3-dimensional images were acquired. The surface roughness was again measured after ultrasonic tooth brushing in order to evaluate the influence of brushing on the surface roughness. The statistical analysis was performed with Mann-Whitney test and t-test using a 95% level of confidence. Results: The chemically polished group showed a statistically lower mean surface roughness in comparison to the mechanically polished group (P = .0045) and the specimens polymerized under the atmospheric pressure presented a more significant difference (P = .0138). After brushing, all of the groups, except the mechanically polished group, presented rougher surfaces and showed no statistically significant differences between groups. Conclusion: Although the surface roughness increased after brushing, the chemical polishing technique presented an improved surface condition in comparison to the mechanical polishing technique.

Velocity Measurements of Slurry Flows in CMP Process by Particle Image Velocimetry (Particle Image Velocimetry 기법을 이용한 CMP 공정의 Slurry유동 분석)

  • Kim Mun-Ki;Yoon Young-Bin;Koh Young-Ho;Hong Chang-Gi;Shin Sang-Hee
    • Journal of the Korean Society for Precision Engineering
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    • v.23 no.5 s.182
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    • pp.59-67
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    • 2006
  • Chemical Mechanical Polishing(CMP) in semiconductor production is characterized its output property by Removal Rate(RR) and Non-Uniformity(NU). Some previous works show that RR is determined by production of pressure and velocity and NU is also largely affected by velocity of flowfield during CMP. This study is about the direct measurement of velocity of slurry during CMP and whole flowfield upon the non-groove pad by Particle Image Velocimetry(PIV). Typical PIV system is modified adequately for inspecting CMP and slurry flowfield is measured by changing both pad rpm and carrier rpm. We performed measurement with giving some variation in the kinds of pad. The results show that the flowfield is majorly determined not by Carrier but by Pad in the case of non-groove pad.

Velocity and Friction Force Distribution in Rotary CMP Equipment (회전형 CMP장비의 속도 및 마찰력 분포 해석)

  • Kim, Hyeong Jae;Jeong, Hae Do;Lee, Eung Suk;Sin, Yeong Jae
    • Journal of the Korean Society for Precision Engineering
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    • v.20 no.5
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    • pp.39-39
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    • 2003
  • As the design rules in semiconductor manufacturing process become more and more stringent, the higher degree of planarization of device surface is required for a following lithography process. Also, it is great challenge for chemical mechanical polishing to achieve global planarization of 12” wafer or beyond. To meet such requirements, it is essential to understand the CMP equipment and process itself. In this paper, authors suggest the velocity distribution on the wafer, direction of friction force and the uniformity of velocity distribution of conventional rotary CMP equipment in an analytical method for an intuitive understanding of variation of kinematic variables. To this end, a novel dimensionless variable defined as “kinematic number” is derived. Also, it is shown that the kinematic number could consistently express the velocity distribution and other kinematic characteristics of rotary CMP equipment.

Velocity and Friction Force Distribution in Rotary CMP Equipment (회전형 CMP장비의 속도 및 마찰력 분포 해석)

  • 김형재;정해도;이응숙;신영재
    • Journal of the Korean Society for Precision Engineering
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    • v.20 no.5
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    • pp.29-38
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    • 2003
  • As the design rules in semiconductor manufacturing process become more and more stringent, the higher degree of planarization of device surface is required for a following lithography process. Also, it is great challenge for chemical mechanical polishing to achieve global planarization of 12” wafer or beyond. To meet such requirements, it is essential to understand the CMP equipment and process itself. In this paper, authors suggest the velocity distribution on the wafer, direction of friction force and the uniformity of velocity distribution of conventional rotary CMP equipment in an analytical method for an intuitive understanding of variation of kinematic variables. To this end, a novel dimensionless variable defined as “kinematic number” is derived. Also, it is shown that the kinematic number could consistently express the velocity distribution and other kinematic characteristics of rotary CMP equipment.

Predicting and Interpreting Quality of CMP Process for Semiconductor Wafers Using Machine Learning (머신러닝을 이용한 반도체 웨이퍼 평탄화 공정품질 예측 및 해석 모형 개발)

  • Ahn, Jeong-Eon;Jung, Jae-Yoon
    • The Journal of Bigdata
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    • v.4 no.2
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    • pp.61-71
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    • 2019
  • Chemical Mechanical Planarization (CMP) process that planarizes semiconductor wafer's surface by polishing is difficult to manage reliably since it is under various chemicals and physical machinery. In CMP process, Material Removal Rate (MRR) is often used for a quality indicator, and it is important to predict MRR in managing CMP process stably. In this study, we introduce prediction models using machine learning techniques of analyzing time-series sensor data collected in CMP process, and the classification models that are used to interpret process quality conditions. In addition, we find meaningful variables affecting process quality and explain process variables' conditions to keep process quality high by analyzing classification result.

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