A golden template self-generating method for patterned wafer inspection

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

A golden block based self-refining scheme for repetitive patterned wafer inspections

This paper presents a novel technique for detecting possible defects in two-dimensional wafer images with repetitive patterns using prior knowledge. It has a learning ability that is able to create a golden block database from the wafer image itself, modify and refine its content when used in further inspections. The extracted building block is stored as a golden block for the detected pattern. When new wafer images with the same periodical pattern arrives, we do not have to re-calculate its periods and building block. A new building block can be derived directly from the existing golden block after eliminating alignment differences. If the newly derived building block has better quality than the stored golden block, then the golden block is replaced with the new building block. With the proposed algorithm, our implementation shows that a significant amount of processing time is saved. And the storage overhead of golden templates is also reduced significantly by storing golden blocks only

반도체 제조 공정을 위한 GAN 기반 이종 이미지 정렬 체계

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

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program

Direct Printing/Patterning of Key Components for Biosensor Devices

Recently, biosensor devices, especially wearable devices for monitoring human health, have attracted significant interests and meanwhile, they have a huge market. These wearable biosensor devices usually consist of several key components, including microfluidics, biosensing elements and power supply. Though advanced sensing platforms have been extensively explored, high manufacturing fee and lack of practical functions are the main reasons that most of devices and techniques are still out of reach for potential users. This dissertation focuses on fabricating these key components for biosensor devices via advanced printing/patterning techniques, such as inkjet-printing and nanoimprinting. These fabrication techniques can be potentially extended to roll-to-roll manufacturing system, allowing for low fabrication costs. Using UV-assisted nanoimprint lithography, flexible microfluidic devices were fabricated with thiol-ene click photopolymer on polymeric substrate. As for sensing elements, inkjet-printed electrodes were applied for electrochemical detections of multiple analytes. Here, inkjet-printed Au electrodes were applied for measuring salmonella concentration with magnetic beads. Glucose and cortisol sensing were developed with inkjet-printed graphene electrodes. These two sensors were compatible with “smart band-aid” platform for wearable monitoring. With synthetic skin, the real-time monitoring of glucose concentration was achieved, and the effect of flow rate was examined in detail. Inkjet-printed electrodes can be easily customized for various applications, though their resolutions are mostly limited to ~20 microns. It is hard to develop materials within nanoscale resolution via inkjet-printing. To develop nanostructured materials, nanoimprint lithography is introduced as a direct patterning method. Several kinds of metal oxide multilayer woodpile nanostructured electrodes were developed. The aspect ratio of the final structure can be easily customized by the number of layers. Furthermore, we examined the performance of these woodpile electrodes in electrochemical applications. For example, CeO2 woodpile electrodes were used for enzymatic glucose sensors, while TiO2 woodpile electrodes were applied as lithium-ion electrodes. The structure-processing combination can lead to efficient use of these electroactive materials. Finally, we utilized solvent-assisted nanoimprint lithography to process cellulose nanomaterials into nanostructure. Cellulose, as a major component of plant, is the most abundant biomaterial in nature. The development of patterned cellulose films can be potentially used as novel, green substrates in many applications, including wearable biosensing devices

Reference-free detection of semiconductor assembly defect

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

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

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

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