• 제목/요약/키워드: Semiconductor Defect

검색결과 254건 처리시간 0.03초

LPP(Landing Plug Poly) CMP Induced Defect 제거에 관한 연구 (A Study on the Removal of LPP CMP Induced Defect)

  • 오평원;최재건;최용수;최근민;송용욱
    • 한국전기전자재료학회:학술대회논문집
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    • 한국전기전자재료학회 2004년도 하계학술대회 논문집 Vol.5 No.1
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    • pp.235-238
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    • 2004
  • 본 연구는 반도체소자 제조공정에 적용되는 CMP공정 중 LPP(Landing Plug Poly) Contact간의 소자 분리를 위해 진행되는 LPP CMP의 후 세정 과정에서 유발되는 방사형 Defect 제거에 관한 내용이다. 방사형 Defect은 LPP CMP 후에 노출되는 BPSG, Poly, Nitride Film과 연마재로 사용되는SiO2 입자, 후 세정과정에서 적용되는 SC-1, DHF, $NH_4OH$ Chemical과 Brush와의 상호작용에 의해 발생되며, Cleaning시의 산성 분위기 하에서 각 물질간의 pH와 Zeta Potential의 차이에서 기인한다. 이 Defect을 제거하기 위해 LPP CMP후에 Film 표면에 노출되는 각 물질의 표면 특성과 CMP 후 오염입자의 흡착과 재 흡착에 영향을 미치는 Electrostatic force와 Van der Waals force, PVA Brush에 의한 Mechanical force의 상호작용을 고려하여 최적 후 세정 조건을 제시 하였다.

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AOI 데이터를 이용한 효과적인 Defect Size Distribution 구축방법: 반도체와 LCD생산 응용 (Effective Construction Method of Defect Size Distribution Using AOI Data: Application for Semiconductor and LCD Manufacturing)

  • 하정훈
    • 산업공학
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    • 제21권2호
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    • pp.151-160
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    • 2008
  • Defect size distribution is a probability density function for the defects that occur on wafers or glasses during semiconductor/LCD fabrication. It is one of the most important information to estimate manufacturing yield using well-known statistical estimation methods. The defects are detected by automatic optical inspection (AOI) facilities. However, the data that is provided from AOI is not accurate due to resolution of AOI and its defect detection mechanism. It causes distortion of defect size distribution and results in wrong estimation of the manufacturing yield. In this paper, I suggest a size conversion method and a maximum likelihood estimator to overcome the vague defect size information of AOI. The methods are verified by the Monte Carlo simulation that is constructed as similar as real situation.

ESPI를 이용한 반도체 패키지 내부결함 검사에 관한 연구 (A Study on the Inner Defect Inspection for Semiconductor Package by ESPI)

  • 정승택;김경석;양승필;정현철;이유황
    • 대한기계학회:학술대회논문집
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    • 대한기계학회 2003년도 추계학술대회
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    • pp.1442-1447
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    • 2003
  • Computer is a very powerful machine which is widely using for data processing, DB construction, peripheral device control, image processing etc. Consequently, many researches and developments have progressed for high performance processing unit, and other devices. Especially, the core units such as semiconductor parts are rapidly growing so that high-integration, high-performance, microminiat turization is possible. The packaging in the semiconductor industry is very important technique to de determine the performance of the system that the semiconductor is used. In this paper, the inspection of the inner defects such as delamination, void, crack, etc. in the semiconductor packages is studied. ESPI which is a non-contact, non-destructive, and full-field inspection method is used for the inner defect inspection and its results are compared with that of C-Scan method.

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인공지지체 불량 검출을 위한 딥러닝 모델 성능 비교에 관한 연구 (A Comparative Study on Deep Learning Models for Scaffold Defect Detection)

  • 이송연;허용정
    • 반도체디스플레이기술학회지
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    • 제20권2호
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    • pp.109-114
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    • 2021
  • When we inspect scaffold defect using sight, inspecting performance is decrease and inspecting time is increase. We need for automatically scaffold defect detection method to increase detection accuracy and reduce detection times. In this paper. We produced scaffold defect classification models using densenet, alexnet, vggnet algorithms based on CNN. We photographed scaffold using multi dimension camera. We learned scaffold defect classification model using photographed scaffold images. We evaluated the scaffold defect classification accuracy of each models. As result of evaluation, the defect classification performance using densenet algorithm was at 99.1%. The defect classification performance using VGGnet algorithm was at 98.3%. The defect classification performance using Alexnet algorithm was at 96.8%. We were able to quantitatively compare defect classification performance of three type algorithms based on CNN.

CNN 알고리즘을 이용한 인공지지체의 3D프린터 출력 시 실시간 출력 불량 탐지 시스템에 관한 연구 (A Study on Real-Time Defect Detection System Using CNN Algorithm During Scaffold 3D Printing)

  • 이송연;허용정
    • 반도체디스플레이기술학회지
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    • 제20권3호
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    • pp.125-130
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    • 2021
  • Scaffold is used to produce bio sensor. Scaffold is required high dimensional accuracy. 3D printer is used to manufacture scaffold. 3D printer can't detect defect during printing. Defect detection is very important in scaffold printing. Real-time defect detection is very necessary on industry. In this paper, we proposed the method for real-time scaffold defect detection. Real-time defect detection model is produced using CNN(Convolution Neural Network) algorithm. Performance of the proposed model has been verified through evaluation. Real-time defect detection system are manufactured on hardware. Experiments were conducted to detect scaffold defects in real-time. As result of verification, the defect detection system detected scaffold defect well in real-time.

반도체 패키지 내부결함 평가 알고리즘의 성능 향상 (Performance Advancement of Evaluation Algorithm for Inner Defects in Semiconductor Packages)

  • 김창현;홍성훈;김재열
    • 한국공작기계학회논문집
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    • 제15권6호
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    • pp.82-87
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    • 2006
  • Availability of defect test algorithm that recognizes exact and standardized defect information in order to fundamentally resolve generated defects in industrial sites by giving artificial intelligence to SAT(Scanning Acoustic Tomograph), which previously depended on operator's decision, to find various defect information in a semiconductor package, to decide defect pattern, to reduce personal errors and then to standardize the test process was verified. In order to apply the algorithm to the lately emerging Neural Network theory, various weights were used to derive results for performance advancement plans of the defect test algorithm that promises excellent field applicability.

결함제어를 통한 열전 반도체 연구 동향 (Defect Engineering for High-Performance Thermoelectric Semiconductors)

  • 민유호
    • 한국전기전자재료학회논문지
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    • 제35권5호
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    • pp.419-430
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    • 2022
  • Defects in solids play a vital role on thermoelectric properties through the direct impacts of electronic band structure and electron/phonon transports, which can improve the electronic and thermal properties of a given thermoelectric semiconductor. Defects in semiconductors can be divided into four different types depending on their geometric dimensions, and thus understanding the effects on thermoelectric properties of each type is of a vital importance. This paper reviews the recent advances in the various thermoelectric semiconductors through defect engineering focusing on the charge carrier and phonon behaviors. First, we clarify and summarize each type of defects in thermoelectric semiconductors. Then, we review the recent achievements in thermoelectric properties by applying defect engineering when introducing defects into semiconductor lattices. This paper ends with a brief discussion on the challenges and future directions of defect engineering in the thermoelectric field.

Growth and Dissolve of Defects in Boron Nitride Nanotube

  • Jun Ha, Lee;Won Ha, Mun
    • 한국반도체및디스플레이장비학회:학술대회논문집
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    • 한국반도체및디스플레이장비학회 2004년도 춘계학술대회 발표 논문집
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    • pp.59-62
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    • 2004
  • The defect formation energy of boron nitride (BN) nanotubes is investigated using molecular-dynamics simulation. Although the defect with tetragon-octagon pairs (4-88-4) is favored in the flat cap of BN nanotubes, BN clusters, and the growth of BN nanotubes, the formation energy of the 4-88-4 defect is significantly higher than that of the pentagon-heptagon pairs (5-77-5) defect in BN nanotubes. The 5-77-5 defect reduces the effect of the structural distortion caused by the 4-88-4 defect, in spite of homoelemental bonds. The instability of the 4-88-4 defect generates the structural transformation into BNNTs with no defect at about 1500 K.

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블록 기반 클러스터링과 히스토그램 카이 제곱 거리를 이용한 반도체 결함 원인 진단 기법 (Cause Diagnosis Method of Semiconductor Defects using Block-based Clustering and Histogram x2 Distance)

  • 이영주;이정진
    • 한국멀티미디어학회논문지
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    • 제15권9호
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    • pp.1149-1155
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    • 2012
  • 본 논문에서는 반도체 산업 영상에서 반도체의 결함 원인 진단 기법을 제안한다. 제안 기법은 먼저 결함 영상에 대한 특징 데이터베이스를 구축한다. 다음으로 결함 영상과 입력 영상을 블록 단위로 영역 분할을 수행한 후 컬러 히스토그램을 계산하여 블록들 사이의 히스토그램 카이 제곱 거리를 이용한 블록 유사성을 측정한다. 다음으로 각 영상에서 탐색된 블록들에 대하여 클러스터링을 수행하여 영역을 연결된 객체 단위로 군집한다. 마지막으로 각 클러스터들의 특징을 추출하여 클러스터 간 유사성 측정으로 가장 유사성이 높은 결함 영상을 특징 DB에서 탐색하여 결함 원인 정보와 함께 제시한다. 검색 결과 유사도 상위 n개의 영상 중에서 입력 영상과 동일한 범주의 결함을 갖는 영상이 검색되는 비율을 구하여 제안 기법의 정확성을 검증하였다. n = 1, 2, 3에 대해서 결함 범주에 상관없이 검색 정확도는 모두 100%로 제안 기법은 실제 산업 응용이 가능한 정확한 검색 결과를 보여주었다.

(-201)면 산화갈륨 단결정 기판 미세 결함 분석 (Characterizations of Microscopic Defect Distribution on (-201) Ga2O3 Single Crystal Substrates)

  • 최미희;신윤지;조성호;정운현;정성민;배시영
    • 한국전기전자재료학회논문지
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    • 제35권5호
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    • pp.504-508
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    • 2022
  • Single crystal gallium oxide (Ga2O3) has been an emerging material for power semiconductor applications. However, the defect distribution of Ga2O3 substrates needs to be carefully characterized to improve crystal quality during crystal growth. We analyzed the type and the distribution of defects on commercial (-201) Ga2O3 substrates to get a basic standard prior to growing Ga2O3 crystals. Etch pit technique was employed to expose the type of defects on the Ga2O3 substrates. Synchrotron white beam X-ray topography was also utilized to observe the defect distribution by a nondestructive manner. We expect that the observation of defect distribution with three-dimensional geometry will also be useful for other crystal planes of Ga2O3 single crystals.