a convolutional autoencoder approach for feature extraction in virtual metrology

Abstract Exploiting the huge amount of data collected by industries is definitely one of the main challenges of the so-called Big Data era. In this sense, Machine Learning has gained growing attention in the scientific community, as it allows to extract valuable information by means of statistical predictive models trained on historical process data. In Semiconductor Manufacturing, one of the most extensively employed data-driven applications is Virtual Metrology, where a costly or unmeasurable variable is estimated by means of cheap and easy to obtain measures that are already available in the system. Often, these measures are multi-dimensional, so traditional Machine Learning algorithms cannot handle them directly. Instead, they require feature extraction, that is a preliminary step where relevant information is extracted from raw data and converted into a design matrix. Features are often hand-engineered and based on specific domain knowledge. Moreover, they may be difficult to scale and prone to information loss, affecting the effectiveness and maintainability of machine learning procedures. In this paper, we present a Deep Learning method for semi-supervised feature extraction based on Convolutional Autoencoders that is able to overcome the aforementioned problems. The proposed method is tested on a real dataset for Etch rate estimation. Optical Emission Spectrometry data, that exhibit a complex bi-dimensional time and wavelength evolution, are used as input

머신 러닝 기반의 낸드 플래시 칩 eFuse 구성 생성 자동화 방법론

학위논문(석사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2019. 8. 유승주.Post fabrication process is becoming more and more important as memory technology becomes complex, in the bid to satisfy target performance and yield across diverse business domains, such as servers, PCs, automotive, mobiles, and embedded devices, etc. Electronic fuse adjustment (eFuse optimization and trimming) is a traditional method used in the post fabrication processing of memory chips. Engineers adjust eFuse to compensate for wafer inter-chip variations or guarantee the operating characteristics, such as reliability, latency, power consumption, and I/O bandwidth. These require highly skilled expert engineers and yet take significant time. This paper proposes a novel machine learning-based method of automatic eFuse configuration to meet the target NAND flash operating characteristics. The proposed techniques can maximally reduce the expert engineers workload. The techniques consist of two steps: initial eFuse generation and eFuse optimization. In the first step, we apply the variational autoencoder (VAE) method to generate an initial eFuse configuration that will probably satisfy the target characteristics. In the second step, we apply the genetic algorithm (GA), which attempts to improve the initial eFuse configuration and finally achieve the target operating characteristics. We evaluate the proposed techniques with Samsung 64-Stacked vertical NAND (VNAND) in mass production. The automatic eFuse configuration takes only two days to complete the implementation.메모리 공정 기술이 발전하고 비즈니스 시장이 다양해 짐에 따라 웨이퍼 수율을 높이고 비즈니스 특성 목표를 만족하기 위한 후 공정 과정이 매우 중요해 지고 있다. 전기적 퓨즈 조절 방식(이-퓨즈 최적화 및 트림)은 메모리 칩 후 공정 과정에서 사용되는 전통적인 방식이다. 엔지니어는 이-퓨즈 조절을 통해 웨이퍼 상의 칩들 간의 초기 특성의 변화를 보상하거나, 신뢰성, 레이턴시, 파워 소모, 그리고 I/O 대역폭 등의 칩 목표 특성을 보장한다. 이-퓨즈 조절 업무는 다수의 숙련된 엔지니어가 필요하고 또한 상당히 많은 시간을 소모한다. 본 논문에서는 낸드 플래시 칩의 동작 특성 목표를 얻기 위한 기계 학습 기반의 이-퓨즈 자동 생성 기술을 제안하고, 해당 기술은 엔지니어의 작업시간을 획기적으로 단축시킬 수 있다. 논문의 기술은 두 단계로 구성 된다. 첫 번째 단계에서는 variational autoencoder (VAE) 기술을 적용하여 목표하는 동작 특성을 만족시키는 초기 이-퓨즈 구성을 생성한다. 두 번째 단계에서는 유전 알고리즘을 적용하여 초기 생성된 이-퓨즈 구성에 대하여 목표하는 성능 특성과의 정합성을 추가로 개선하여 최종적으로 목표하는 성능 특성을 얻는다. 논문의 평가는 실제 양산중인 삼성 64단 브이낸드 제품을 이용하여 진행하였다. 논문의 이-퓨즈 자동화 생성 기술은 2일 이내의 구현 시간만이 소요된다.Contents I. Introduction..........................................................................1 II. Background..........................................................................4 2.1. NAND Flash Block Architecture..................................................4 2.2. NAND Cell Vth Distribution........................................................5 2.3. eFuse Operation of NAND Flash Chip.......................................6 III. Basic Idea and Background...............................................7 3.1. Basic Idea.......................................................................................7 3.2. Background: Variational Autoencoder........................................10 IV. Initial eFuse Generation: VAE-Based Dual Network....14 V. eFuse Optimization: Genetic Algorithm..........................17 VI. Experimental Results.........................................................21 6.1. Experimental Setup......................................................................21 6.2. Initial eFuse Generation Results................................................23 6.3. eFuse Optimization Results........................................................26 6.4. Discussion.....................................................................................29 VII. Related Work..................................................................31 VIII. Conclusion.......................................................................33Maste

Investigating effects of behavioural flexibility and neuroplasticity on acclimation success of outcrossed Chinook salmon (Oncorhynchus tshawytscha): applications in aquaculture

After generations of artificial selection and domestication of animals for consumption, unintended consequences such as inbreeding depression have impacted production via impacts on growth and survival. Outcrossing is a common method used to negate these effects and introduce variation to the broodstock. This thesis aims to assess how animals respond to novel environments both behaviourally and transcriptionally to captivity. Seven wild-domestic hybrid stocks of Chinook salmon (Oncorhynchus tshawytscha) and a highly inbred domesticated stock population included as control were used in this study to determine what, if any, effects outbreeding has on the variation of behavioural and neural transcriptional phenotypes produced. Two behavioural assays were completed on the same set of individuals as juveniles and as adults to test for the occurrence of traits involved in the acclimation to new environments via traits such as sociality, exploration, activity, predator responsiveness and neophilia. These behaviours were then contrasted against performance at each time point and across life-history stage. We found inter-population variation in four distinct behavioural types and changes across ontogeny. In each life stage we demonstrated certain behaviours are linked to performance. Whole brain samples were collected from juvenile and adult fish to assess via qRT-PCR mRNA expression of genes associated with a variety of neural traits purportedly involved in acclimation: stress responses, synaptoplasticity and neurogenesis. A subset of transcriptional profiles and candidate genes related to neural stress responses and neuroplasticity were able to predict performance, however, there were no stock differences in their expression. As more animals are brought into captivity for consumption or conservation it is important to consider how behavioural and neural responses integrate to affect animal survival and develop efficient screening processes

Intelligent Escape of Robotic Systems: A Survey of Methodologies, Applications, and Challenges

Intelligent escape is an interdisciplinary field that employs artificial intelligence (AI) techniques to enable robots with the capacity to intelligently react to potential dangers in dynamic, intricate, and unpredictable scenarios. As the emphasis on safety becomes increasingly paramount and advancements in robotic technologies continue to advance, a wide range of intelligent escape methodologies has been developed in recent years. This paper presents a comprehensive survey of state-of-the-art research work on intelligent escape of robotic systems. Four main methods of intelligent escape are reviewed, including planning-based methodologies, partitioning-based methodologies, learning-based methodologies, and bio-inspired methodologies. The strengths and limitations of existing methods are summarized. In addition, potential applications of intelligent escape are discussed in various domains, such as search and rescue, evacuation, military security, and healthcare. In an effort to develop new approaches to intelligent escape, this survey identifies current research challenges and provides insights into future research trends in intelligent escape.Comment: This paper is accepted by Journal of Intelligent and Robotic System

Computer Image Analysis Based Quantification of Comparative Ihc Levels of P53 And Signaling Associated With the Dna Damage Repair Pathway Discriminates Between Inflammatory And Dysplastic Cellular Atypia

Epithelial oncogenesis is believed to be generally associated with the accumulation over time of an increasing number of mitotic errors until a threshold number of mutations required for the initiation of cancer is achieved. Preemption of cancer through the morphologic detection of dysplastic cells, i.e. cells with a number of mitotic errors that are still below the threshold for cancer, followed by their surgical removal or eradication, has had an enormous impact on reducing the incidence of cancer of the uterine cervix, skin and colon worldwide, but this strategy has been much less successful with cancers in most other body sites. Inflammation is a relatively common occurrence in the epithelium and is far more common than cancer. A major current obstacle to the preemption of carcinoma is distinguishing morphologically atypical epithelial cells in the presence of inflammation (inflammatory atypia) that mimic dysplasia from morphologically atypical epithelial cells that are truly dysplastic. Formation of double stranded breaks in DNA (DSBs) is an accepted etiology for carcinoma and is, therefore, expected to be associated with dysplasia. Utilizing both algorithmic and artificial intelligence-based computer image analysis of IHC levels, we document the unexpected finding that phosphorylation of molecular markers associated with DSBs is consistently correlated with non-dysplastic iv inflammatory atypia in both squamous (oral cavity) and glandular (Barrett’s metaplasia) epithelia. Using these same image analysis methods, we further show that quantitative immunohistochemistry of the ratio of p-Chk2, a marker of DSB’s, and for mutational failure of the DNA damage repair pathway (p53) required for the proper response to DSBs can distinguish between inflammatory and dysplastic cellular atypia. The ability to use quantitative means to reliably distinguish between inflammatory and dysplastic atypia may facilitate the use of cytological screening for dysplasia to prevent cancer in numerous body sites

Application of Meta-Analysis and Machine Learning Methods to the Prediction of Methane Production from In Vitro Mixed Ruminal Micro-Organism Fermentation

peer-reviewedIn vitro gas production systems are utilized to screen feed ingredients for inclusion in ruminant diets. However, not all in vitro systems are set up to measure methane (CH4) production, nor do all publications report in vitro CH4. Therefore, the objective of this study was to develop models to predict in vitro CH4 production from total gas and volatile fatty acid (VFA) production data and to identify the major drivers of CH4 production in these systems. Meta-analysis and machine learning (ML) methodologies were applied to a database of 354 data points from 11 studies to predict CH4 production from total gas production, apparent DM digestibility (DMD), final pH, feed type (forage or concentrate), and acetate, propionate, butyrate and valerate production. Model evaluation was performed on an internal dataset of 107 data points. Meta-analysis results indicate that equations containing DMD, total VFA production, propionate, feed type and valerate resulted in best predictability of CH4 on the internal evaluation dataset. The ML models far exceeded the predictability achieved using meta-analysis, but further evaluation on an external database would be required to assess generalization ability on unrelated data. Between the ML methodologies assessed, artificial neural networks and support vector regression resulted in very similar predictability, but differed in fitting, as assessed by behaviour analysis. The models developed can be utilized to estimate CH4 emissions in vitro

An update on novel approaches for diagnosis and treatment of SARS-CoV-2 infection

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has made a serious public health and economic crisis worldwide which united global efforts to develop rapid, precise, and cost-efficient diagnostics, vaccines, and therapeutics. Numerous multi-disciplinary studies and techniques have been designed to investigate and develop various approaches to help frontline health workers, policymakers, and populations to overcome the disease. While these techniques have been reviewed within individual disciplines, it is now timely to provide a cross-disciplinary overview of novel diagnostic and therapeutic approaches summarizing complementary efforts across multiple fields of research and technology. Accordingly, we reviewed and summarized various advanced novel approaches used for diagnosis and treatment of COVID-19 to help researchers across diverse disciplines on their prioritization of resources for research and development and to give them better a picture of the latest techniques. These include artificial intelligence, nano-based, CRISPR-based, and mass spectrometry technologies as well as neutralizing factors and traditional medicines. We also reviewed new approaches for vaccine development and developed a dashboard to provide frequent updates on the current and future approved vaccines

Introduction to Data Ethics

An Introduction to data ethics, focusing on questions of privacy and personal identity in the economic world as it is defined by big data technologies, artificial intelligence, and algorithmic capitalism. Originally published in The Business Ethics Workshop, 3rd Edition, by Boston Acacdemic Publishing / FlatWorld Knowledge

THE IMPACT OF WASTEWATER CHEMISTRY AND FLOW CHARACTERISTICS ON HYDROGEN SULFIDE CONCENTRATION

Hydrogen sulfide (H2S) is a naturally occurring, highly toxic gas that is formed from the decomposition of sulfur compounds. At low concentrations, H2S is an irritant for the eyes, respiratory and gastrointestinal tract. At higher H2S concentrations, it produces neurological impairment with dizziness, headache, and loss of consciousness. Mortality was reported to reach 6 percent following exposure to elevated concentrations. H2S is generated in different industrial processes. In wastewater collection and treatment plants, H2S is a common source of concrete and metal corrosion that has resulted in huge economic loss. In this project, the factors leading to the generation of H2S in wastewater has been studied. Different parameters were measured from Al-Saad wastewater treatment plant, UAE. Wastewater samples were collected for measurement of chemical properties affecting H2S generation in the laboratory. Significant parameters were identified to be temperature of wastewater, humidity, total sulfur, and wastewater depth, using correlation analysis with corresponding H2S concentration. Statistical equations formed from this regression analysis was used to estimate the H2S concentration in headspace. A neural network model with an R-sq value of 0.9 was developed to predict H2S emission at different wastewater unit operations. Studying the generation of H2S in wastewater would in effect help alleviate the maintenance costs of wastewater treatment systems

A Hierarchical, Fuzzy Inference Approach to Data Filtration and Feature Prioritization in the Connected Manufacturing Enterprise

The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency or interpretability by the human data scientist or decision maker. This dissertation, contextualized in the connected manufacturing enterprise, presents an original Fuzzy Approach to Feature Reduction and Prioritization (FAFRAP) approach that is designed to assist the data scientist in filtering and prioritizing data for inclusion in supervised machine learning models. A set of sequential filters reduces the initial set of independent variables, and a fuzzy inference system outputs a crisp numeric value associated with each feature to rank order and prioritize for inclusion in model training. Additionally, the fuzzy inference system outputs a descriptive label to assist in the interpretation of the feature’s usefulness with respect to the problem of interest. Model testing is performed using three publicly available datasets from an online machine learning data repository and later applied to a case study in electronic assembly manufacture. Consistency of model results is experimentally verified using Fisher’s Exact Test, and results of filtered models are compared to results obtained by the unfiltered sets of features using a proposed novel metric of performance-size ratio (PSR)