• 제목/요약/키워드: Semiconductor Packaging

검색결과 280건 처리시간 0.019초

반도체 패키징 공정에서 발생하는 실리콘 슬러지의 재활용을 통한 Si@SiO2 제조 및 에폭시 몰딩 컴파운드로의 응용 (Synthesis of Silica Coated Silicon Substrate by Recycling Silicon Sludge Generated in Semiconductor Packaging Process and Their Application to Epoxy Molding Compound)

  • 추연룡;강다희;김하영;임지수;박규식;제갈석;윤창민
    • 유기물자원화
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    • 제32권3호
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    • pp.57-66
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    • 2024
  • 본 연구에서는 반도체 패키징 공정에서 발생하는 실리콘 슬러지(Silicon-sludge, S-sludge)에 실리카층을 코팅(Silica coated silicon-sludge, SS-sludge)하였으며, 이를 에폭시 몰딩 컴파운드(Epoxy molding compound, EMC)의 필러로 적용하였다. 상세히는, 산세처리를 통해 S-sludge의 금속불순물을 제거하였으며, 졸-겔법을 통해 SS-sludge를 제조하였다. SS-sludge는 에폭시 고분자, 경화제 및 카본블랙과 혼합하여 EMC(Silica coated silicon-sludge EMC, SS-sludge EMC)로 제조되었다. 적외선 카메라를 통한 방열 특성 분석 결과, 제조된 SS-sludge EMC는 58.5℃의 가장 높은 표면 온도를 나타내었다. 이는 SS-sludge의 주성분인 실리콘의 높은 열전도도(150W/mK) 및 실리카 코팅에 의해 EMC의 방열 특성이 향상되었기 때문이다. 본 연구를 통해, 반도체 패키징 공정에서 발생하는 실리콘 슬러지를 고부가가치를 지닌 반도체 패키징용 EMC의 필러로 재활용할 수 있는 방안을 제시하였다.

A Study of Wire Sweep During Encapsulation of Semiconductor Chips

  • Han, Se-Jin;Huh, Yong-Jeong
    • 마이크로전자및패키징학회지
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    • 제7권4호
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    • pp.17-22
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    • 2000
  • In this paper, methods to analyze wire sweep during the semiconductor chip encapsulation have been studied. The wire sweep analysis is used to analyze the deformation of bonding wires that connect the chip to the leadframe during encapsulation. The analysis is done using either analytical solutions or numerical simulation. The analytical solution is used for rough but fast calculation of wire sweep. The results from the numerical simulation are closest to the experimental results.

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효율적 수치해석기법을 이용한 반도체 페키지의 열방출 해석 (Efficient Approach to Thermal Modeling for IC Packages)

  • Seung Mo Kim;Choon Heung Lee
    • 마이크로전자및패키징학회지
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    • 제6권2호
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    • pp.31-36
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    • 1999
  • An efficient method for thermal modeling of QFP is Proposed. Thermal measurement data are given to verify the method. In parallel with the experiment, an exact full 3-D model calculation is also provided. One fonds that there is an excellent agreement between validation data and the efficient model data.

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A Study on the/ Correlation Between Board Level Drop Test Experiment and Simulation

  • Kang, Tae-Min;Lee, Dae-Woong;Hwang, You-Kyung;Chung, Qwan-Ho;Yoo, Byun-Kwang
    • 마이크로전자및패키징학회지
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    • 제18권2호
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    • pp.35-41
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    • 2011
  • Recently, board level solder joint reliability performance of IC packages during drop impact becomes a great concern to semiconductor and electronic product manufacturers. The handheld electronic products are prone to being dropped during their useful service life because of their size and weight. The IC packages are susceptible to solder joint failures, induced by a combination of printed circuit board (PCB) bending and mechanical shock during impact. The board level drop testing is an effective method to characterize the solder joint reliability performance of miniature handheld products. In this paper, applying the JEDEC (JESD22-B111) standard present a finite element modeling of the FBGA. The simulation results revealed that maximum stress was located at the outermost solder ball in the PCB or IC package side, which consisted well with the location of crack initiation observed in the failure analysis after drop reliability tests.

인공지능 반도체 및 패키징 기술 동향 (Artificial Intelligence Semiconductor and Packaging Technology Trend)

  • 김희주;정재필
    • 마이크로전자및패키징학회지
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    • 제30권3호
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    • pp.11-19
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    • 2023
  • 최근 Chat GPT와 같은 인공지능 (Artificial Intelligence, AI) 기술의 급격한 발전에 따라 AI 반도체의 중요성이 강조되고 있다. AI 기술은 빅데이터 처리, 딥 러닝, 알고리즘 등의 요구사항으로 인해 대용량 데이터를 빠르게 처리할 수 있는 능력을 필요로 한다. 그러나 AI 반도체는 대규모 데이터를 처리하는 과정에서 과도한 전력 소비와 데이터 병목현상 문제가 발생한다. 반도체 전공정의 초미세공정이 물리적 한계에 도달함에 따라, AI 반도체의 연산을 위한 최신 패키징 기술이 요구되는 추세이다. 본 고에서는 AI 반도체에 적용가능한 인터포저, TSV, 범핑, Chiplet, 하이브리드 본딩 패키징 기술에 대해서 기술하였다. 이러한 기술들은 AI 반도체의 전력 효율과 연산 속도를 향상시키는데 기여할 것으로 기대된다.

차세대 반도체용 유-무기 나노 복합재료의 에폭시 수지변화에 따른 흡습특성 (Moisture Absorption Properties of Organic-Inorganic Nano Composites According to the Change of Epoxy Resins for Next Generation Semiconductor Packaging Materials)

  • 김환건;김동민
    • 반도체디스플레이기술학회지
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    • 제12권1호
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    • pp.23-28
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    • 2013
  • Epoxy resins are widely used in microelectronics packaging such as printed circuit board and encapsulating for semiconductor manufacturing. Water can diffuse into and through the epoxy matrix systems and moisture absorption at boarding interfaces of matrix resin systems can lead to a hydrolysis at the interfaces resulting in delamination of encapsulating materials. In the study, the changes of diffusion coefficient and moisture content ratio of epoxy resin systems with nano-sized fillers according to the change of liquid type epoxy resins were investigated. RE-304S, RE-310S, RE-810NM and HP-4032D as a epoxy resin, Kayahard AA as a hardener, and 1B2MI as a catalyst were used in these epoxy resin systems. After curing, moisture content ratios were measured with time under the 85 and 85% relative humidity condition using a thermo-hydrostat. The maximum moisture absorption ratio and diffusion coefficient of EMC decrease with the filler content. It can be seen that these decreases are due to the increase of filler surface area and the decrease of moisture through channel with the content of nano-sized filler.

Study on Fault Diagnosis and Data Processing Techniques for Substrate Transfer Robots Using Vibration Sensor Data

  • MD Saiful Islam;Mi-Jin Kim;Kyo-Mun Ku;Hyo-Young Kim;Kihyun Kim
    • 마이크로전자및패키징학회지
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    • 제31권2호
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    • pp.45-53
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    • 2024
  • The maintenance of semiconductor equipment is crucial for the continuous growth of the semiconductor market. System management is imperative given the anticipated increase in the capacity and complexity of industrial equipment. Ensuring optimal operation of manufacturing processes is essential to maintaining a steady supply of numerous parts. Particularly, monitoring the status of substrate transfer robots, which play a central role in these processes, is crucial. Diagnosing failures of their major components is vital for preventive maintenance. Fault diagnosis methods can be broadly categorized into physics-based and data-driven approaches. This study focuses on data-driven fault diagnosis methods due to the limitations of physics-based approaches. We propose a methodology for data acquisition and preprocessing for robot fault diagnosis. Data is gathered from vibration sensors, and the data preprocessing method is applied to the vibration signals. Subsequently, the dataset is trained using Gradient Tree-based XGBoost machine learning classification algorithms. The effectiveness of the proposed model is validated through performance evaluation metrics, including accuracy, F1 score, and confusion matrix. The XGBoost classifiers achieve an accuracy of approximately 92.76% and an equivalent F1 score. ROC curves indicate exceptional performance in class discrimination, with 100% discrimination for the normal class and 98% discrimination for abnormal classes.