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Nano-scale gap filling and mechanism of deposit-etch-deposit process for phase-change material

Chinese Physics B 2012年第11期21卷 335-339页

作者：任万春刘波宋志棠向阳辉王宗涛张北超封松林 State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Micro-system and Information TechnologyChinese Academy of Sciences Semiconductor Manufacturing International Corporation University of the Chinese Academy of Sciences

Ge2Sb2Te5 gap filling is one of the key processes for phase-change random access memory manufacture. Physical vapor deposition is the mainstream method of Ge2Sb2Te5 film deposition due to its advantages of film quality, purity, and accurate composition control. However,the conventional physical vapor deposition process cannot meet the gap- filling requirement with the critical device dimension scaling down to 90 nm or below. In this study, we find that the deposit-etch-deposit process shows better gap-filling capability and scalability than the single-step deposition process, especially at the nano-scale critical dimension. The gap-filling mechanism of the deposit-etch-deposit process was briefly discussed. We also find that re-deposition of phase-change material from via the sidewall to via the bottom by argon ion bombardment during the etch step was a key ingredient for the final good gap filling. We achieve void-free gap filling of phase-change material on the 45-nm via the two-cycle deposit-etch-deposit process. We gain a rather comprehensive insight into the mechanism of deposit-etch-deposit process and propose a potential gap-filling solution for over 45-nm technology nodes for phase-change random access memory.

关键词： deposit-etch deposit process single step deposit gap filling re-deposition

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BioNano Genome Map Resource for Oryza sativa *** and indica and Its Application in Rice Genome Sequence Correction and gap filling

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Molecular Plant 2017年第6期10卷 895-898页

作者： Ping Chen Xinyun Jing Baosheng Liao Yan Zhu Jiang Xu Renyi Liu Yinhong Zhao Xuan Li Key Laboratory of Synthetic Biology CAS Center for Excellence in Molecular Plant Sciences Institute of Plant Physiology and Ecology Chinese Academy of Sciences Shanghai 200032 China Institute of Chinese Materia Medica China Academy of Chinese Medical Sciences Beijing 100700 China Shanghai Centre for Plant Stress Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai 201602 China China National Center for Biotechnology Development Building 4 No.16 Xisihuanzhonglu Beijing 100036 China University of Chinese Academy of Sciences Beijing 100049 China

Dear Editor As one of the most important staple food crops worldwide, rice is among the first plant species whose genomes were sequenced. The reference genome for Oryza sativa ssp. japonica variety Nipponbare was completed by the International Rice Genome Sequencing Project （IRGSP） using a bacterial artificial chromosome （BAC）-based cloning strategy （Matsumoto et al., 2005）.

关键词： BioNano Genome Map Resource Genome Sequence Correction gap filling

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gap filling of Net Ecosystem CO<sub>2</sub>Exchange (NEE) above Rain-Fed Maize Using Artificial Neural Networks (ANNs)

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Journal of Software Engineering and Applications 2021年第5期14卷 150-171页

作者： Babak Safa Timothy J. Arkebauer Qiuming Zhu Andy Suyker Suat Irmak Daugherty Water for Food Global Institute at the University of Nebraska Lincoln USA Department of Agronomy & Horticulture University of Nebraska-Lincoln Lincoln USA Department of Computer Science University of Nebraska-Omaha Omaha USA School of Natural Resources University of Nebraska-Lincoln Lincoln USA Biological Systems Engineering Department University of Nebraska-Lincoln Lincoln USA

The eddy covariance technique is an accurate and direct tool to measure the Net Ecosystem Exchange (NEE) of carbon dioxide. However, sometimes conditions are not amenable to measurements using this technique. Thus, different methods have been developed to allow gap-filling and quality assessment of eddy covariance data sets. In this study first, two different Artificial Neural Networks (ANNs) approaches, the Multi-layer Perceptron (MLP) trained by the Back-Propagation (BP) algorithm, and the Radial Basis Function (RBF), were used to fill missing NEE data measured above rain-fed maize at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, Nebraska. The gap-filled data were then compared by different statistical indices to gap-filled data obtained with the technique suggested by Suyker and Verma in 2005 [S&V method], and the ANN approach presented by Papale in 2003. The results showed that the RBF network was able to find better fits for missing values compared to the MLP (BP) network and S&V method. In addition, unlike the S&V method, which depends on different gap-filling procedures over the year;the structure of RBF and MLP (BP) networks was constant. However, data analysis indicated Papale’s approach gave better fits than the RBF and MLP (BP) methods. Thus, based on this work, Papale’s approach is the best method to estimate the missing data;though the applied statistical indices, which were used for model evaluation, show little difference between Papale’s approach and the RBF and MLP (BP).

关键词： gap filling Net Ecosystem Exchange of Carbon Dioxide Artificial Neural Networks Eddy Covariance System

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The efficacy of Kriging spatial interpolation for filling temporal gaps in daily air temperature data: A case study in northeast China

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Research in Cold and Arid Regions 2016年第5期8卷 441-449页

作者： YanLin Zhang XiaoLi Chang Ji Liang DongLiang Luo RuiXia He National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology Hunan University of Science and Technology State Key Laboratory of Frozen Soils Engineering Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences

Quality-controlled and serially complete daily air temperature data are essential to evaluating and modelling the influences of climate change on the permafrost in cold regions. Due to malfunctions and location changes of observing stations, temporal gaps （i.e., missing data） are common in collected datasets. The objective of this study was to assess the efficacy of Kriging spatial interpolation for estimating missing data to fill the temporal gaps in daily air temperature data in northeast China. A cross-validation experiment was conducted. Daily air temperature series from 1960 to 2012 at each station were estimated by using the universal Kriging （UK） and Kriging with an external drift （KED）, as appropriate, as if all the ob-servations at a given station were completely missing. The temporal and spatial variation patterns of estimation uncertainties were also checked. Results showed that Kriging spatial interpolation was generally desirable for estimating missing data in daily air temperature, and in this study KED performed slightly better than UK. At most stations the correlation coefficients （R2） between the observed and estimated daily series were 〉0.98, and root mean square errors （RMSEs） of the estimated daily mean （Tmean）, maximum （Tmax）, and minimum （Tmin） of air temperature were 〈3 ℃. However, the estimation quality was strongly affected by seasonality and had spatial variation. In general, estimation uncertainties were small in summer and large in winter. On average, the RMSE in winter was approximately 1 ℃ higher than that in summer. In addition, estimation uncertainties in mountainous areas with complex terrain were significantly larger than those in plain areas.

关键词： daily air temperature gap filling Kriging spatial interpolation northeast China

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基于长短读数结合的序列组装相关方法研究

基于长短读数结合的序列组装相关方法研究

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作者：关婷河南理工大学

学位级别：硕士

从头基因序列组装的目的是获取完整且准确的基因组序列,这有助于后续的基因识别、结构变异检测和基因组比对等研究。Scaffolding是从头序列组装中的一个关键组装步骤,主要是对contigs序列进行排序和定向,生成更加连续和完整的scaffolds... 详细信息

从头基因序列组装的目的是获取完整且准确的基因组序列,这有助于后续的基因识别、结构变异检测和基因组比对等研究。Scaffolding是从头序列组装中的一个关键组装步骤,主要是对contigs序列进行排序和定向,生成更加连续和完整的scaffolds序列。而gap填充过程是针对scaffolds中contigs之间的gap进行填充,这使得scaffolds可以更加完整,从而获得更多的生物学信息。针对当前scaffolding方法和gap填充方法的研究现状,考虑到传统长读数长度长但精确度低和短读数长度短但精度高的特点,本文进行了基于长短读数结合的序列组装方法研究,同时采用两种数据的优点应用在序列组装中,这有助于获得更加完整的组装结果。(1)本文提出了一种基于长短读数结合的scaffolding方法,该方法利用了短读数的高准确性和长读数的长度优势,提高了对重复区域的识别。首先是数据预处理阶段,使用比对工具完成读数和contigs之间的比对之后,为了减少无效比对信息对于后续组装的影响,算法从多个角度对比对信息进行了筛选。接着构建scaffold图,为了提高图中高可靠边的优势,SLHSD根据比对到每对contigs上的读数将边分为了三种类型,并按照优先级将他们添加到图中。SLHSD使用了一种更加精确的,结合多种比对特征的边权重计算方法,不仅考虑了读数和contigs之间的比对数量,而且在基于比对方向和比对长度的基础上,考虑了支持两个contigs连接的读数数量原始值和实际值之间的关系来决定是否添加边,以及如何对一条边加权。然后,由于读数本身具有错误率和重复区域,得到的scaffold图有可能会非常复杂,SLHSD采用线性规划模型对图中的方向和位置冲突进行了检测和消除。SLHSD方法在五个数据集上进行了实验验证,实验结果表明,SLHSD可组装得到长度更长且组装错误相对较少的scaffolds序列。(2)为了得到更加完整的序列,本文提出了一种基于马尔科夫链的gap填充方法。首先为了避免传统长读数的高错误率的问题,将高精度的短读数比对到测序错误率较高的长读数上完成对长读数的纠错。然后,识别scaffolds中的gap区域并抽取出所有未知碱基,将原本带有gap区域的scaffolds序列分割转换为仅带有正常碱基的子序列,并完成长读数和子序列的比对。接着,根据比对信息,即长读数对子序列的连接情况,将比对到每处gap的长读数分为三个不同的集合。对于一个gap和与其相关联的三个长读数集合,首先尝试使用Racon求解一致性序列,若有解可直接完成填充,若无解则采用基于马尔科夫链的碱基预测方法,对已知候补序列构建状态转移矩阵,完成gap处的序列填充。通过实验比对分析,该方法在某些指标上要优于其他方法。图19幅,表20个,参考文献104篇。

关键词：序列组装混合组装 scaffolding scaffold图 gap filling

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