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Predicting the computing resources required by simulation applications can provide a more reasonable resource-allocation scheme for efficient execution. Existing prediction methods based on machine learning, such as classification/regression, typically must accurately predict the runtime of simulation applications and select the optimal computing resource allocation scheme by sorting the length of the simulation runtime. However, the ranking results are easily affected by the simulation runtime prediction accuracy. This study proposes a time-sensitive learning-to-rank (LTR) approach for cloud simulations resource prediction. First, we use the Shapley additive explanation (SHAP) value from the field of explainable artificial intelligence (XAI) to analyze the impact of relevant factors on the simulation runtime and to extract the feature dimensions that significantly affect the simulation runtime. Second, by modifying the target loss function of the rankboost algorithm and training a time-sensitive LTR model based on simulation features, we can accurately predict the computing resource allocation scheme that maximizes the execution efficiency of simulation applications. Compared with the traditional machine learning prediction algorithm, the proposed method can improve the average sorting performance by 3%–48% and can accurately predict the computing resources required for the simulation applications to execute in the shortest amount of time.

•PV/T performance is improved by optimizing plasmonic nanofluid filters via MFmodel.•Optical characteristics of core-shell nanofluids are investigated theoretically.•A numerical model is used to study effect of nanofluid parameters on PV/T performance.•The optimization procedure tends to enhance electrical efficiency firstly.•Au@SiO2-Ag@SiO2blended nanofluids show a cost-effectivity for application in filters.

•Optimal parameter of Au/water (20 nm, 49 ppm, 20 mm) & Au/EG (20 nm, 33 ppm, 20 mm).•Au nanoparticles capped with PEG or PVP are prepared and dispersed in water or EG.•Au + PVP/EG filter shows better stability than Au + PVP/water proved by stability tests.•Indoor flowing setup used to test performance of PV/T system with optimal filter.•MF of 1.95 & exergy efficiency of 13.98% achieved filtered by Au + PVP/EG (29.2 ppm).

As integrated circuits for high performance CMOS devices scale down to <=10 nm dimension, further reductions in cap thickness to reduce capacitance are required for the Cu barrier while maintaining sufficient mechanical strength, low leakage, high dielectric breakdown, and fabrication integration robustness. This paper presents the development of a second generation robust Low Hydrogen SiCN to enable cap thickness reduction to <=10 nm by simply altering/reducing the hydrogen concentration in the SiCN film. This is achieved by the simple addition of Hydrogen precursor in the plasma deposition chemistry

Acronym disambiguation is the process of determining the correct expansion of an acronym in given context, which can assist many downstream natural language processing tasks. Typically, existing methods on this task will directly perform semantic comparisons between the candidate expansions and the original sentence, ignoring the relevance of contextual information to expansions. To solve this issue, this paper proposes a context-aware acronym disambiguation method with Siamese BERT network (ContextAD). First, we combine each candidate expansion with corresponding acronym’s context to form a new sentence set. Then, the new and original sentences are input into a Siamese BERT network that can obtain the semantic similarity. The new sentences and the separate candidate expansions are input into the Siamese BERT network, respectively, along with the original sentences, which can obtain another semantic similarity. Finally, the two different semantic similarities are combined to determine the most suitable expansion. We quantify the improvement of our proposed ContextAD model against a state-of-the-art baseline using the public dataset of the shared tasks of acronym disambiguation (AD) held under AAAI-2021 workshop on SDU and show that it achieves a better performance based on the same BERT model.

The paper discusses the properties of silicon nitride and silicon oxide spacer films from the perspectives of ALD (Atomic Layer Deposition) and MLD (Molecular Layer Deposition) techniques, precursor chemistries and compositional stability after implant anneals and spacer sculpting reactive ion etching processes. Advanced imaging techniques like EELS (Electron Energy Loss Spectroscopy) and SIMS (Secondary Ion Mass Spectrometry) techniques have been used to analyze sidewall spacer processes. Special attention has been paid to the microloading effect - the dependence of thin film deposition rate on pattern density. The effect of ALD deposition parameters on film electrical characteristics like dielectric breakdown and leakage are also delineated.

Polyurethane (PU) pad debris is identified as one of the major polish residue defects in Cu CMP processes when a barrier pad is conditioned. AES analysis of the debris confirms the organic nature of such defects while FT-IR analysis reveals the characteristic peaks of polyurethane from the pad debris. Hybrid cleans (i.e. acidic plus basic cleans) and basic cleans can both remove the pad debris effectively. The efficiency of basic-only cleans can be improved by increasing time and/or chemistry concentration in the brush modules. The PU pad can endure harsh chemical environments at both low and high pH without any detectable decomposition or morphological changes. The advantages and disadvantages of acidic vs. basic cleans are discussed and the cleaning mechanisms of pad debris PR are elucidated.

Abstract

Large-scale complex system modeling and simulation involves multidisciplinary knowledge. It is usually necessary to create models using different modeling languages and methods. Therefore, a simulation system built by these models has the characteristics of heterogeneous and hierarchical structure. It will be a great challenge in designing and controlling simulation execution. This paper proposed a hierarchical modeling method and designed a synchronous control algorithm based on synchronous points array. Then we implemented a collaborative simulation framework based on distributed computing environment. The result illustrates this framework provides an efficient mechanism to support hierarchical modeling. With the rate of computing load to communication load increasing, the parallelism will be higher, thus the performance of the simulation system can be promoted.

As integrated circuits for high performance CMOS devices scale down to <=10 nm dimension, further reductions in cap thickness to reduce capacitance are required for the Cu barrier while maintaining sufficient mechanical strength, low leakage, high dielectric breakdown, and fabrication integration robustness. This paper presents the development of a second generation robust Low Hydrogen SiCN to enable cap thickness reduction to <=10 nm by simply altering/reducing the hydrogen concentration in the SiCN film. This is achieved by the simple addition of Hydrogen precursor in the plasma deposition chemistry

The paper discusses the properties of silicon nitride and silicon oxide spacer films from the perspectives of ALD (Atomic Layer Deposition) and MLD (Molecular Layer Deposition) techniques, precursor chemistries and compositional stability after implant anneals and spacer sculpting reactive ion etching processes. Advanced imaging techniques like EELS (Electron Energy Loss Spectroscopy) and SIMS (Secondary Ion Mass Spectrometry) techniques have been used to analyze sidewall spacer processes. Special attention has been paid to the microloading effect - the dependence of thin film deposition rate on pattern density. The effect of ALD deposition parameters on film electrical characteristics like dielectric breakdown and leakage are also delineated.

Polyurethane (PU) pad debris is identified as one of the major polish residue defects in Cu CMP processes when a barrier pad is conditioned. AES analysis of the debris confirms the organic nature of such defects while FT-IR analysis reveals the characteristic peaks of polyurethane from the pad debris. Hybrid cleans (i.e. acidic plus basic cleans) and basic cleans can both remove the pad debris effectively. The efficiency of basic-only cleans can be improved by increasing time and/or chemistry concentration in the brush modules. The PU pad can endure harsh chemical environments at both low and high pH without any detectable decomposition or morphological changes. The advantages and disadvantages of acidic vs. basic cleans are discussed and the cleaning mechanisms of pad debris PR are elucidated.