Doctor of Philosophy

dissertationIn order to ensure high production yield of semiconductor devices, it is desirable to characterize intermediate progress towards the final product by using metrology tools to acquire relevant measurements after each sequential processing step. The metrology data are commonly used in feedback and feed-forward loops of Run-to-Run (R2R) controllers to improve process capability and optimize recipes from lot-to-lot or batch-to-batch. In this dissertation, we focus on two related issues. First, we propose a novel non-threaded R2R controller that utilizes all available metrology measurements, even when the data were acquired during prior runs that differed in their contexts from the current fabrication thread. The developed controller is the first known implementation of a non-threaded R2R control strategy that was successfully deployed in the high-volume production semiconductor fab. Its introduction improved the process capability by 8% compared with the traditional threaded R2R control and significantly reduced out of control (OOC) events at one of the most critical steps in NAND memory manufacturing. The second contribution demonstrates the value of developing virtual metrology (VM) estimators using the insight gained from multiphysics models. Unlike the traditional statistical regression techniques, which lead to linear models that depend on a linear combination of the available measurements, we develop VM models, the structure of which and the functional interdependence between their input and output variables are determined from the insight provided by the multiphysics describing the operation of the processing step for which the VM system is being developed. We demonstrate this approach for three different processes, and describe the superior performance of the developed VM systems after their first-of-a-kind deployment in a high-volume semiconductor manufacturing environment

RL-MD: A Novel Reinforcement Learning Approach for DNA Motif Discovery

The extraction of sequence patterns from a collection of functionally linked unlabeled DNA sequences is known as DNA motif discovery, and it is a key task in computational biology. Several deep learning-based techniques have recently been introduced to address this issue. However, these algorithms can not be used in real-world situations because of the need for labeled data. Here, we presented RL-MD, a novel reinforcement learning based approach for DNA motif discovery task. RL-MD takes unlabelled data as input, employs a relative information-based method to evaluate each proposed motif, and utilizes these continuous evaluation results as the reward. The experiments show that RL-MD can identify high-quality motifs in real-world data.Comment: This paper is accepted by DSAA2022. The 9th IEEE International Conference on Data Science and Advanced Analytic

Geochemical characteristics of Cambrian bitumen and Cambrian-Ordovician source rocks in the Keping area, NW Tarim Basin

The Cambrian Yuertus Formation and Ordovician Saergan and Yingan formation source rocks, which TOC contents of 0.38%–4.30%, are well developed in the Keping area of the Tarim Basin. Reservoir bitumen had been found in the Cambrian Wusongger Formation and Shayilike Formation. In this study, the geochemical characteristics of the bitumen and source rocks were analyzed through biomarkers for oil-source correlation. The results show that the characteristics of the bitumen and Yuertus Formation source rocks are similar. Comparatively, the Yuertus Formation source rocks and bitumen have lower Pr/Ph values and higher C28/C29 regular steranes values. The maturity characteristics and depositional environment of the Cambrian source rocks in the Keping area and the platform basin areas are similar. Plots of Ph/n-C18versus Pr/n-C17, Ts/(Ts+Tm) versus 4-/1-MDBT (methyl dibenzothiophene), and DBT/P (dibenzothiophene/phenanthrene) versus Pr/Ph distinguish the bitumen and source rocks well. As an original plot, we found that the Fla/Py (fluoranthene/pyrene) versus MP/P (methyl-phenanthrene/phenanthrene) intersection plot can be used to identify the possible sources of polycyclic aromatic hydrocarbons (PAHs) to a certain extent and can distinguish between the Cambrian and Ordovician source rocks in this study. Comprehensive analysis revealed that the bitumen samples most likely originated from the Yuertus Formation source rocks. It was also found that the biomarker characteristics such as the shape type of the C27-C28-C29 regular steranes, triarylosteranes, and triarylosteroids are not applicable to distinguishing the Cambrian and Ordovician source rocks in the Keping area. These research findings provide references for studying the Lower Paleozoic oil-source correlation in the platform in the Tarim Basin

Analysis of Chengdu air quality pollution based on logistic sequential multi-classification of distance between classes

Air quality is closely related to people’s health. It is very important to analyze the pollutants affecting air quality. The sequential Logistic multi-classification method of inter class distance was used to analyze the air quality data of Chengdu from May 2019 to April 2020. Based on the inter class distance, the multi classification problem was transformed into multiple binary classification problems, and then the binary classification Logistic was used based on the sequential principle. Finally, the correct rate after stepwise regression was used to analyze the pollutants affecting air quality. Experimental results show that the four types of pollutants PM2.5, PM10, NO2 and O3 have the greatest comprehensive impact on air quality of Chengdu. The government should strengthen joint monitoring of these types of pollutants and formulate corresponding policies to reduce pollutants

Machine Unlearning Method Based On Projection Residual

Machine learning models (mainly neural networks) are used more and more in real life. Users feed their data to the model for training. But these processes are often one-way. Once trained, the model remembers the data. Even when data is removed from the dataset, the effects of these data persist in the model. With more and more laws and regulations around the world protecting data privacy, it becomes even more important to make models forget this data completely through machine unlearning. This paper adopts the projection residual method based on Newton iteration method. The main purpose is to implement machine unlearning tasks in the context of linear regression models and neural network models. This method mainly uses the iterative weighting method to completely forget the data and its corresponding influence, and its computational cost is linear in the feature dimension of the data. This method can improve the current machine learning method. At the same time, it is independent of the size of the training set. Results were evaluated by feature injection testing (FIT). Experiments show that this method is more thorough in deleting data, which is close to model retraining.Comment: This paper is accepted by DSAA2022. The 9th IEEE International Conference on Data Science and Advanced Analytic

Pose Guided Human Image Synthesis with Partially Decoupled GAN

Pose Guided Human Image Synthesis (PGHIS) is a challenging task of transforming a human image from the reference pose to a target pose while preserving its style. Most existing methods encode the texture of the whole reference human image into a latent space, and then utilize a decoder to synthesize the image texture of the target pose. However, it is difficult to recover the detailed texture of the whole human image. To alleviate this problem, we propose a method by decoupling the human body into several parts (\eg, hair, face, hands, feet, \etc) and then using each of these parts to guide the synthesis of a realistic image of the person, which preserves the detailed information of the generated images. In addition, we design a multi-head attention-based module for PGHIS. Because most convolutional neural network-based methods have difficulty in modeling long-range dependency due to the convolutional operation, the long-range modeling capability of attention mechanism is more suitable than convolutional neural networks for pose transfer task, especially for sharp pose deformation. Extensive experiments on Market-1501 and DeepFashion datasets reveal that our method almost outperforms other existing state-of-the-art methods in terms of both qualitative and quantitative metrics.Comment: 16 pages, 14th Asian Conference on Machine Learning conferenc

Distribution of bacterial keratitis and emerging resistance to antibiotics in China from 2001 to 2004

Chen Zhang, Yanchuang Liang, Shijing Deng, Zhiqun Wang, Ran Li, Xuguang SunDepartment of Ocular Microbiology, Beijing Institute of Ophthalmology, Beijing Tongren hospital, Capital University of Medical Science, BeijingObjective: To study on the distribution of bacterial keratitis isolates and the resistance to antibiotics in China from 2001 to 2004.Methods: 1985 specimens from the bacterial keratitis at the Beijing Tong Ren Eye Center were cultured and identified. In vitro susceptibility testing of positive isolates to antibiotics was determined by the Kirby-Bauer disk diffusion method and interpreted according to Clinical and Laboratory Standards Institute.Results: Out of 1985 specimens, 279 were culture positive. The percentage of positive culture was 14.06%. Gram-positive cocci and gram-negative bacilli represented 42.65% (119/270) and 35.13% (98/279) respectively. Pseudomonas sp. was the most common organism (20.07%), followed by Corynebacterium sp. (16.85%) and Staphylococcus epidermidis (13.98%). Resistance to ofloxacin, ciprofloxacin, levofloxacin, and tobramycin was 20.2%, 35.9%, 15.5%, and 29.4% respectively. Gram-negative bacilli showed higher resistance to ciprofloxacin. Staphycoccus sp. revealed significant resistance to ciprofloxacin. Streptococcus sp. showed high resistance to tobramycin. The resistance of isolates from older patients (≥60Y) to ciproloxacin, levofloxacin, and tobramycin was higher than that from adult patients (>14 to 59Y).Conclusion: Staphylococcus sp., Pseudomonas sp., and Corynebacterium sp. were the most common bacterial keratitis isolates in China. Attentions should be paid to the increase of the resistance to levofloxacin.Keywords: bacteria keratitis resistanc