Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review

A growing need exists for efficient and accurate methods for detecting defects in semiconductor materials and devices. These defects can have a detrimental impact on the efficiency of the manufacturing process, because they cause critical failures and wafer-yield limitations. As nodes and patterns get smaller, even high-resolution imaging techniques such as Scanning Electron Microscopy (SEM) produce noisy images due to operating close to sensitivity levels and due to varying physical properties of different underlayers or resist materials. This inherent noise is one of the main challenges for defect inspection. One promising approach is the use of machine learning algorithms, which can be trained to accurately classify and locate defects in semiconductor samples. Recently, convolutional neural networks have proved to be particularly useful in this regard. This systematic review provides a comprehensive overview of the state of automated semiconductor defect inspection on SEM images, including the most recent innovations and developments. 38 publications were selected on this topic, indexed in IEEE Xplore and SPIE databases. For each of these, the application, methodology, dataset, results, limitations and future work were summarized. A comprehensive overview and analysis of their methods is provided. Finally, promising avenues for future work in the field of SEM-based defect inspection are suggested.Comment: 16 pages, 12 figures, 3 table

Roadmap on semiconductor-cell biointerfaces.

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world

Nanoscale Optical and Correlative Microscopies for Quantitative Characterization of DNA Nanostructures

Methods to engineer nanomaterials and devices with uniquely tailored properties are highly sought after in fields such as manufacturing, medicine, energy, and the environment. The macromolecule deoxyribonucleic acid (DNA) enables programmable self-assembly of nanostructures with near arbitrary shape and size and with unprecedented precision and accuracy. Additionally, DNA can be chemically modified to attach molecules and nanoparticles, providing a means to organize active materials into devices with unique or enhanced properties. One particularly powerful form of DNA-based self-assembly, DNA origami, provides robust structures with the potential for nanometer-scale resolution of addressable sites. DNA origami are assembled from one large DNA scaffold strand and many unique, short staple strands; each staple programmatically binds the scaffold at several distant domains, and the coordinated interactions of many staples with the scaffold act to fold the scaffold into a desired shape. The utility of DNA origami has been demonstrated through multiple applications, such as plasmonic and photonic devices, electronic device patterning, information storage, drug delivery, and biosensors. Despite the promise of DNA nanotechnology, few products have successfully translated from the laboratory to industry. Achieving high yield and high-precision synthesis of stable DNA nanostructures is one of the biggest challenges to applications of DNA nanostructures. For adoption in manufacturing, methods to measure and inspect assembled structures (i.e. metrology) are essential. Common high-resolution imaging techniques used to characterize DNA nanostructures, such as atomic force microscopy and transmission electron microscopy, cannot facilitate high-throughput characterization, and few studies have been directed towards the development of improved methods for nanoscale metrology. DNA-PAINT super-resolution microscopy enables high-resolution, multiplexed imaging of reactive sites on DNA nanostructures and offers the potential for inline optical metrology. In this work, nanoscale metrologies utilizing DNA-PAINT were developed for DNA nanostructures and applied to characterize DNA origami arrays and single site defects on DNA origami. For metrology of DNA origami arrays, an embedded, multiplexed optical super-resolution methodology was developed to characterize the periodic structure and defects of two-dimensional arrays. Images revealed the spatial arrangement of structures within the arrays, internal array defects, and grain boundaries between arrays, enabling the reconstruction of arrays from the images. The nature of the imaging technique is also highly compatible with statistical methods, enabling rapid statistical analysis of synthesis conditions. To obtain a greater understanding of DNA origami defects at the scale of individual strands, correlative super-resolution and atomic force microscopies were enabled through the development of a simple and flexible method to bind DNA origami directly to cover glass, simultaneously passivating the surface to single-stranded DNA. High-resolution, correlative microscopy was performed to characterize DNA origami, and spatial correlation in super-resolution optical and topographic images of 5 nm was achieved, validating correlative microscopy for single strand defect metrology. Investigations of single strand defects showed little correlation to structural defects on DNA origami, revealing that most site defects occur on strands that are present in the structure, contrary to prior reports. In addition, the results suggest that the structural stability of DNA origami was decreased by DNA-PAINT imaging. The presented work demonstrated the development and application of advanced characterization techniques for DNA nanostructures, which will accelerate fundamental research and applications of DNA nanotechnology

The Advent of Application Specific Integrated Circuits (ASIC)-MEMS within the Medical System

Medical healthcare has become one of the fastest growing and largest industries in the world. More and more people are aware of the precious and important life. At the same time, personal disposable income increases and awareness of disease prevention increases. It allows the healthcare industry to maintain high growth rates. Micro-electro- mechanical systems (MEMS) is one of the most revolutionary semiconductor components. The advent of Application Specific Integrated Circuits (ASIC)-MEMS has created a new era for the healthcare industry. The medical Micro LED detects the blood vessel position with the emission light source and repositions the blood flow state of the blood vessel. Micro LED mainly uses the MEMS micro-fabrication technology to micronize, array, and thin film the traditional LED crystal film. This article will explore how to use MEMS wafers to redefine the needs of the healthcare market and open up new growth opportunities for healthcare applications. With the shift from first-hand medical devices from the hospital business to personal use, miniaturization, economics, reliability and battery life have become new demands in the healthcare market

Machine Learning-based Predictive Maintenance for Optical Networks

Optical networks provide the backbone of modern telecommunications by connecting the world faster than ever before. However, such networks are susceptible to several failures (e.g., optical fiber cuts, malfunctioning optical devices), which might result in degradation in the network operation, massive data loss, and network disruption. It is challenging to accurately and quickly detect and localize such failures due to the complexity of such networks, the time required to identify the fault and pinpoint it using conventional approaches, and the lack of proactive efficient fault management mechanisms. Therefore, it is highly beneficial to perform fault management in optical communication systems in order to reduce the mean time to repair, to meet service level agreements more easily, and to enhance the network reliability. In this thesis, the aforementioned challenges and needs are tackled by investigating the use of machine learning (ML) techniques for implementing efficient proactive fault detection, diagnosis, and localization schemes for optical communication systems. In particular, the adoption of ML methods for solving the following problems is explored: - Degradation prediction of semiconductor lasers, - Lifetime (mean time to failure) prediction of semiconductor lasers, - Remaining useful life (the length of time a machine is likely to operate before it requires repair or replacement) prediction of semiconductor lasers, - Optical fiber fault detection, localization, characterization, and identification for different optical network architectures, - Anomaly detection in optical fiber monitoring. Such ML approaches outperform the conventionally employed methods for all the investigated use cases by achieving better prediction accuracy and earlier prediction or detection capability

지역혁신체제의 다양성과 후발 지역의 경제 추격

학위논문(박사) -- 서울대학교대학원 : 사회과학대학 경제학부, 2022. 8. 이근.혁신은 경제 성장과 경제 추격에 있어 중추적인 역할을 해왔다. 동아시아 국가들이 보여준 것과 같이 혁신은 중진국 함정 단계를 넘어 경제적 추격을 지속하는데 있어서, 가격이나 비용적인 요소보다 더 중요한 요인이었다. 국가의 혁신역량 혹은 혁신의 효율성을 나타내기 위해 국가 혁신체제라는 개념이 고안되었는데, 이는 슘페터 경제학의 핵심적인 개념이다. 하지만 국가 단위의 연구에 초점이 맞춰진 국가혁신체제로는 국가 내 여러 지역의 이질적인 특성을 고려하여 분석할 수 없다. 그렇기 때문에 지역혁신체제라는 분석틀이 필요했고 1990년대부터 지역혁신체제의 개념이 확립되었다. 본 연구에서는 세계 주요 도시의 지역혁신체제 분석을 통해 도시/지역 간 다른 특징들을 살펴보고, 특히 빠른 경제성장을 보이는 추격형 지역들이 선진 지역들과 어떤 다른 특성을 보이는 지에 대해 알아보고자 한다. 본 연구에서는 지역혁신체제를 양적으로 측정하기 위해 7가지의 지표를 사용하는데, 지식의 지역화, 국내화, 국제화 지수를 포함해, 지식 소유권의 토착화 정도, 기술다각화, 지식분권도, 기술사이클 등이다. 국가혁신체제 연구에서는 지식을 창출할 때 국내 지식을 이용하는지 해외 지식을 이용하는지, 두 가지 차원으로만 나누어지지만, 지역 단위의 연구에서는 같은 지역의 지식을 이용하는지, 같은 국가이지만 다른 지역의 지식을 이용하는지, 그리고 다른 국가의 지식을 이용하는지 등 세 가지 차원으로 나누어지기 때문에, 새롭게 국내화 지수라는 개념이 추가되었다. 또한 본 연구에서는 토착 지식이 혁신에 있어 중요한 역할을 하는지에 대해 알아보기 위해, 토착지식을 측정할 수 있는 지식 소유의 토착화정도 변수도 새롭게 만들어 추가하였다. 첫 번째 장에서는 아시아에서 공통적으로 중진국 함정에서 벗어나 빠른 경제성장을 보이고 있는 대만의 타이페이, 중국의 심천, 그리고 말레이시아의 페낭의 지역혁신체제에 대해 비교 연구를 하고, 심천이 타이페이를 페낭보다 더 빠르게 추격할 수 있었던 이유에 대해 지역혁신체제 관점에서 분석한다. 국가혁신체계 연구에서는 후발 국가들이 선진국들을 추격하기 위해서 단주기 기술로의 특화가 중요하게 작용한다고 하였지만, 본 연구에서는 세 지역 모두 단주기 기술에 특화했음에도 불구하고 1인당 GRDP와 경제 성장률에서 차이를 보였다. 이렇게 다른 결과를 보인 이유는 국제화 지수 (외국기술 의존도)가 타이페이와 심천지역에서 낮고, 그리고 지식 소유의 토착화 정도가 페낭보다 높게 나타나기 때문이다. 따라서, 세 지역의 비교 연구를 통해, 지역 간 경제추격에 있어 토착 지식의 증가와 그에 따른 해외 지식에 대한 의존도 감소가 얼마나 중요하게 작용하는 지를 발견할 수 있었다. 두 번째 장에서는 앞 장에서 다루었던 타이페이, 심천, 페낭을 포함한 전 세계 30개 지역의 지역혁신 체제의 특징을 2001년에서 2017년까지의 지역혁신체제 변수를 통해 살펴보고, 클러스터 분석을 통해 어떻게 유형화가 가능한 지에 대해 연구한다. 클러스터 분석 결과, 지역이 단주기 혹은 장주기 기술에 특화하는지, 토착 지식이 큰지 작은지에 따라, 총 네 개의 지역 혁신 체계 그룹으로 분류가 된다. 첫째 유형은 선진국 형으로, 국제화 정도 (해외지식 의존도)가 낮고, 높은 토착소유화, 기술다각화, 및 분권화를 보인다. 추격형 유형은 두가지로 나누어 지는데, 보다 고도화된 유형은 한국이나 대만의 도시와 같이 해외지식 의존도가 낮고, 지식의 토착소유화 정도가 높은 유형이고, 덜 고도화된 유형은 페낭이나 방갈로와 같이 해외지식 의존도가 높고, 토착소유화 정도가 낮은 유형이다. 세 번째 장에서는 두 번째 장에서 클러스터 분석을 통해 나타났던 지역혁신체제 그룹들과 경제성장률의 상관관계를 알기 위해 회귀분석을 진행하였다. 그 결과, 단주기 기술에 특화된 두 가지의 추격형 그룹들이 가장 빠른 경제 성장률을 보이며 선진 지역(장주기 기술 특화∙높은 토착지식)을 빠르게 추격하는 결과를 보여준다. 세 개의 장을 종합하여 살펴보면, 지역 혁신체제 연구에서도 국가 혁신체제 연구의 결과와 마찬가지로, 후발지역들의 추격형 지역 혁신체제의 특징을 확정지울 수 있었다. 특히, 똑 같이 단주기 기술로의 특화하는 후발 지역 간에도 추격성과가 다르게 나타나는 것은, 결국 지식소유의 토착화의 제고와 해외 지식에 대한 의존도를 줄이는 것이 선결 조건임을 밝힌 것이 중요한 공헌이다.Innovation plays a critical role in economic growth and economic catch-up. As Asian countries have witnessed, innovation is more important than price or cost when economies overcome the middle-income trap and sustain their economic growth. National innovation systems (NIS), a key concept for Schumpeterian economies, was introduced to represent the innovation capacity or efficiency of countries. However, given that NIS focuses on national-level analysis, the regional heterogeneities within a nation cannot be easily explained by this concept. To address this problem, a new framework called regional innovation systems (RIS) emerged in the 1990s. This dissertation examines the different innovation-related characteristics of cities/regions around the world using the concept of RIS and reveals the differences between catching-up and advanced regions. This study uses seven variables to numerically measure RIS, namely, knowledge localization, nationalization, internationalization, local ownership of knowledge, technological diversification, knowledge decentralization, and technological cycle time. In NIS analysis, knowledge citation is divided into two dimensions, namely, citing locally invented patents and citing foreign patents, whereas in RIS analysis, three dimensions are employed, namely, local patent citation, national patent citation, and international patent citation. In this way, the new concept of nationalization is added in this RIS research. This study also uses local ownership of knowledge to measure the level of indigenous knowledge in a city/region. The first chapter presents a comparative analysis of the RISs of Taipei in Taiwan, Shenzhen in China, and Penang in Malaysia to understand why Shenzhen is catching up with Taipei much faster than Penang in terms of RIS. In NIS analysis, latecomer economies need to specialize in short cycle technologies. However, this study only focuses on the divergence between per capita GRDP and economic growth rate even if the three aforementioned regions all specialize in the same short-cycle technologies because the levels of internationalization in Taipei and Shenzhen are lower than that of Penang, that is, Taipei and Shenzhen have a lower dependence on foreign knowledge compared with Penang, whereas the local ownership of knowledge for Taipei and Shenzhen is higher than that for Penang. Through this comparative analysis, this study highlights the importance of increasing indigenous knowledge and decreasing reliance on foreign knowledge in regional economic catch-up. The second chapter explores the RIS characteristics of 30 regions over the world to derive a typology of RIS via cluster analysis. On the basis of the cluster analysis results, four groups of RISs are classified depending on whether a region specializes in short- or long-cycle technologies and whether indigenous knowledge is large or small. The first group is the mature RIS group, which has a low level of internationalization (reliance on foreign knowledge) and high levels of local ownership of knowledge, diversification, and decentralization, whereas the second group is the catching-up RIS group, which is further divided into two types. First, cities/countries with more advanced catching-up RIS, such as South Korea and Taiwan, have low reliance on foreign knowledge and high indigenous knowledge. Second, cities/countries with less advanced catching-up RIS, including Penang and Bangalore, have low level of indigenous knowledge and high dependence on foreign knowledge. The third chapter empirically investigates the linkage between the RIS groups resulting from cluster analysis, and economic growth . The catching-up RIS cities/countries that specialize in short-cycle technologies show a faster growth rate compared with others, and catch up with advanced region fast with specialization in long cycle technologies and high indigenous knowledge . By considering the three aforementioned regions, the characteristics of catching-up RIS for latecomer regions as reported in the RIS and NIS analyses are the same. Improving local ownership of knowledge and decreasing reliance on foreign knowledge are prerequisites for regional economic catch-up in regions with different catching-up performances even if the latecomer regions specialize in similar short-cycle technologies.I. Introduction 1 II. Literature Review and Research Questions 3 1. National Innovation Systems 3 2. Regional Innovation Systems and Research Questions 4 3. Definition of RIS Variables 5 III. Case Study of RIS in Asia: Comparing the Regions of Penang, Shenzhen, and Taipei 10 1. Economic Backgrounds of the Three Regions 10 2. Key Aspects of Catch-Up and Hypothesis 14 3. Results 16 4. Three Models of Catching-Up: Taipei, Shenzhen, and Penang 22 5. Concluding Remarks 27 IV. Varieties of RIS and Catching-Up RIS 30 1. Introduction 30 2. Data and Methodology: Cluster Analysis 31 3. Backgrounds of Economies and Hypothesis 32 4. Identifying the Varieties of RIS 35 5. Conclusion 59 V. Linking RIS Groups to Economic Growth 61 1. Introduction 61 2. Cluster analysis 61 3. Literature Review and Hypothesis 63 4. Methodology and Model 64 5. Data 65 6. Result 66 7. Conclusion 69 Ⅵ. Contributions and Limitations 70 1. Key Findings 70 2. Contributions and Limitations 71 References 72 Appendices 78박