323 research outputs found

    A golden template self-generating method for patterned wafer inspection

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    This paper presents a novel golden template self-generating technique for detecting possible defects in periodic two-dimensional wafer images. A golden template of the patterned wafer image under inspection can be obtained from the wafer image itself and no other prior knowledge is needed. It is a bridge between the existing self-reference methods and image-to-image reference methods. Spectral estimation is used in the first step to derive the periods of repeating patterns in both directions. Then a building block representing the structure of the patterns is extracted using interpolation to obtain sub-pixel resolution. After that, a new defect-free golden template is built based on the extracted building block. Finally, a pixel-to-pixel comparison is all we need to find out possible defects. A comparison between the results of the proposed method and those of the previously published methods is presented

    반도체 제조 공정을 위한 GAN 기반 이종 이미지 정렬 체계

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    학위논문(석사)--서울대학교 대학원 :공과대학 기계항공공학부,2019. 8. 김도년.In semiconductor manufacturing process, visual inspection on wafer using template-based detection is widely researched topic. As a prerequisite of detection process, new demand for aligning multimodal image has emerged. To address this issue, this paper proposes a framework with GAN based image translation followed by NCC based template matching algorithm. Different from previous function based approaches, our deep learning based framework effectively transforms an image to another domain where template matching is much easier. Also, for practical usage, we propose a new training data generation strategy which allows our model to train from only 20 pre-aligned images. Experimental results on 4 types of manually aligned data, consisted of 400 pairs of images, demonstrate that our method successfully transforms image regardless of the presence of defect or noise. Also, using transformed image, alignment process with NCC based template matching achieved almost 100% accuracy on every types of image. Moreover, our framework shows great efficiency as it takes only 15 minutes for training and 0.25 seconds per image in test time.반도체 공정에서 템플릿을 이용한 비전 기반의 웨이퍼 검사는 널리 연구되는 분야이다. 이러한 검사 과정의 전제 조건으로 멀티모달 이미지 정렬에 대한 새로운 요구가 대두되었다. 이 문제를 해결하기 위해 본 논문은 GAN을 활용한 이미지 변환과 NCC 기반의 템플릿 정렬 알고리즘을 이용한 프레임워크를 제안한다. 이전의 함수 기반 접근법과 달리 딥러닝 기반 프레임워크는 이미지를 템플릿 정렬이 훨씬 용이한 도메인으로 효과적으로 변환한다. 또한 실용적인 관점에서 고안한 새로운 학습 데이터 생성 방법을 통해 오직 20개의 정렬된 초기 데이터를 통해서 딥러닝 모델을 성공적으로 학습할 수 있다. 각각 100쌍의 이미지로 이루어진 4가지 종류의 수작업으로 정렬한 데이터를 사용한 실험 결과를 통해 고안한 방법이 결함이나 노이즈의 존재여부와 상관없이 효과적으로 이미지를 변환한다는 것을 확인할 수 있다. 또한 변환된 이미지를 사용한 NCC 기반의 템플릿 정렬 알고리즘은 이미지 정렬에서 100%에 가까운 정확도를 보인다. 마지막으로 소요 시간에서 프레임워크는 학습에 15분, 테스트 시 이미지당 0.25 초 만을 소모하며 높은 효율을 보인다.1. Introduction 1 2. Proposed Framework 5 2.1 Training image generation and image preprocessing 6 2.2 GAN based image translation and template matching 9 3. Experimental Results 13 3.1 Performance of image generation 14 3.2 Accuracy of template matching 22 3.3 Running time of framework 24 4. Conclusion 26 References 28 Abstract in Korean 31Maste

    Reference-free detection of semiconductor assembly defect

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    This paper aims at developing a novel defect detection algorithm for the semiconductor assembly process by image analysis of a single captured image, without reference to another image during inspection. The integrated circuit (IC) pattern is usually periodic and regular. Therefore, we can implement a classification scheme whereby the regular pattern in the die image is classified as the acceptable circuit pattern and the die defect can be modeled as irregularity on the image. The detection of irregularity in image is thus equivalent to the detection of die defect. We propose a method where the defect detection algorithm first segments the die image into different regions according to the circuit pattern by a set of morphological segmentations with different structuring element sizes. Then, a feature vector, which consists of many image attributes, is calculated for each segmented region. Lastly, the defective region is extracted by the feature vector classification. © 2005 SPIE and IS&T.published_or_final_versio

    A nanostructured porous silicon based drug delivery device

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    Targeted and controlled delivery of therapeutic agents on demand is pivotal in realising the efficacy of many pharmaceuticals. The design and fabrication of a novel, electrically-addressable, porous structure-based drug delivery device for the controlled release of therapeutic proteins and peptides, are described in this thesis. The initial prototype microdevice design incorporates a porous polysilicon (PPSi) structure as a drug reservoir. Two alternative methods were investigated to fabricate the PPSi structure: i) the chemical stain etching method; ii) a reactive ion etching (RIE) method through a masking template. Random pores, with irregular pore shape and size in the micro- to mesoporous regime (< 50 nm), were obtained using the stain etching method but this method suffered from poor reproducibility and non-uniformity. Two novel RIE approaches were investigated to fabricate ordered PPSi structures; two different masking templates were investigated – a porous anodic alumina (PAA) and a metal mask with hexagonally arranged holes produced by a novel nanosphere lithography (NSL) technique. A quasi-ordered PAA template with pore diameters in the region of 50 nm was fabricated but was not suitable for the subsequent proposed RIE process. By using the NSL technique, quasi-ordered PPSi structures with tapered pore profiles, were obtained. This is the first demonstration of the fabrication of PPSi with ordered pores of sizes in the macropore range of ~ 370 nm.A revised silicon-based prototype microdevice was designed and fabricated. The microdevice incorporates a nanostructured, quasi-ordered porous silicon (PSi) as a drug reservoir and an integrated heater and temperature sensor as an active control mechanism. The PSi structure was fabricated using a modified NSL technique and a Bosch-based RIE process. Hexagonally arranged cylindrical pores with diameters between ~75 nm and ~120 nm, and depths in the range of ~330 nm and 500 nm, were obtained. The novel fabrication techniques investigated here are simple and versatile; both p-type and n-type PSi structures have been successfully fabricated. Proof-of-concept studies, using the revised prototype drug delivery microdevices, suggested that the nanostructured PSi would be suitable for the passive release of an intermediate-sized (~23,000 Dalton) model protein. It is envisaged that the microdevice has the potential to deliver osteoinductive growth factors, on demand, to the site of fracture, in a controlled and sustainable manner, as a first step to an intelligent therapeutic system for skeletal regeneration

    Advanced Applications of Rapid Prototyping Technology in Modern Engineering

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    Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems

    Chip-scale bioassays based on surface-enhanced Raman scattering: fundamentals and applications

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    This work explores the development and application of chip-scale bioassays based on surface-enhanced Raman scattering (SERS) for high throughput and high sensitivity analysis of biomolecules;The size effect of gold nanoparticles on the intensity of SERS is first presented. A sandwich immunoassay was performed using Raman-labeled immunogold nanoparticles with various sizes. The SERS responses were correlated to particle densities, which were obtained by atomic force microscopy (AFM). The response of individual particles was also investigated using Raman-microscope and an array of gold islands on a silicon substrate. The location and the size of individual particles were mapped using AFM;The next study describes a low-level detection of Escherichia coli O157:H7 and simulants of biological warfare agents in a sandwich immunoassay format using SERS labels, which have been termed Extrinsic Raman labels (ERLs). A new ERL scheme based on a mixed monolayer is also introduced. The mixed monolayer ERLs were created by covering the gold nanoparticles with a mixture of two thiolates, one thiolate for covalently binding antibody to the particle and the other thiolate for producing a strong Raman signal;An assay platform based on mixed self-assembled monolayers (SAMs) on gold is then presented. The mixed SAMs were prepared from dithiobis(succinimidyl undecanoate) (DSU) to covalently bind antibodies on gold substrate and oligo(ethylene glycol)-terminated thiol to prevent nonspecific adsorption of antibodies. After the mixed SAMs surfaces, formed from various mole fraction of DSU were incubated with antibodies, AFM was used to image individual antibodies on the surface;The final study presents a collaborative work on the single molecule adsorption of YOYO-I labeled lambda-DNA at compositionally patterned SAMs using total internal reflection fluorescence microscopy. The role of solution pH, lambda-DNA concentration, and domain size was investigated. This work also revealed the potential importance of structural defects
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