Materials, Processes, and Characterization of Extended Air-gaps for the Intra-level Interconnection of Integrated Circuits

Materials, Processes, and Characterization of Extended Air-gaps for the Intra-level Interconnection of Integrated Circuits Seongho Park 157 pages Directed by Dr. Paul A. Kohl and Dr. Sue Ann Bidstrup Allen The integration of an air-gap as an ultra low dielectric constant material in an intra-metal dielectric region of interconnect structure in integrated circuits was investigated in terms of material properties of a thermally decomposable sacrificial polymer, fabrication processes and electrical performance. Extension of the air-gap into the inter-layer dielectric region reduces the interconnect capacitance. In order to enhance the hardness of a polymer for the better process reliabilities, a conventional norbornene-based sacrificial polymer was electron-beam irradiated. Although the hardness of the polymer increased, the thermal properties degraded. A new high modulus tetracyclododecene-based sacrificial polymer was characterized and compared to the norbornene-based polymer in terms of hardness, process reliability and thermal properties. The tetracyclododecene-based polymer was harder and showed better process reliability than the norbornene-based sacrificial polymer. Using the tetracyclododecene-based sacrificial polymer, a single layer Cu/air-gap and extended Cu/air-gap structures were fabricated. The effective dielectric constant of the air-gap and extended air-gap structures were 2.42 and 2.17, respectively. This meets the requirements for the 32 nm node. Moisture uptake of the extended Cu/air-gap structure increased the effective dielectric constant. The exposure of the structure to hexamethyldisilazane vapor removed the absorbed moisture and changed the structure hydrophobic, improving the integration reliability. The integration processes of the air-gap and the extended air-gap into a dual damascene Cu metallization process has been proposed compared to state-of-the-art integration approaches.Ph.D.Committee Chair: Kohl, Paul A.; Committee Co-Chair: Allen, Sue Ann Bidstrup; Committee Member: Carter, W. Brent; Committee Member: Frazier, Albert B; Committee Member: Hess, Dennis; Committee Member: Meredith, Carso

마이크로채널 피셔-트롭쉬 반응기를 활용한 Gas-to-Liquid 공정의 최적 설계에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 화학생물공학부, 2016. 2. 한종훈.For several decades, a gas-to-liquid (GTL) process has been identified as a promising technology for converting abundant natural gas (NG) to clean synthetic fuel. Throughout the GTL process, NG is firstly converted to synthesis gas, mainly composed of hydrogen and carbon monoxide by a syngas reforming process. Syngas is then chemically converted into the liquid fuel by Fischer-Tropsch (FT) reaction process, wherein several carbon atoms in the syngas are oligomerized to form a long chain hydrocarbon product. This hydrocarbon product is also known as FT synfuel, and it contains many hydrocarbon species of carbon number ranging from 1 (methane) to more than 30 (FT wax). FT synfuel has high research octane number (RON) and cetane number (CN) and almost nitrogen and sulfur free. So it is taking a premium position in the fuel market. FT synthesis reaction is known as an strongly exothermic reaction: A large amount of heat, ca. 165kJ per mol of converted CO is generated during the reaction. This heat must be removed to prevent runaway situation and achieve safe isothermal operation of the reactor. Various types of reactors, such as fixed bed, slurry bubble column, circulating fluidized bed, and fixed fluidized bed FT reactors, have been developed and applied to the GTL industries. In order to utilize the FT reaction in off-shore platform, however, more compact, but highly productive, FT reactor is essential. Recently, the concept of micro channel FT reactor has evolved because it can effectively handle large amount of heat based on the high heat exchange surface area per unit volume. It was reported that the heat removal rate in the micro-channel reactor was around 15 times higher than that in a conventional fixed bed reactor. Moreover, micro-channel reactors are good for scale-up perspectives because the wax production can be easily increased by simply adding modularized reactors. It has been reported that the performance of the pilot scale micro-channel reactor was consistent with that of a single micro-channel reactor due to the achievement in isothermal operation. This thesis has addressed the optimal design for GTL processes based on the micro-channel Fischer-Tropsch reactor technology: Modeling of the micro-channel FT reactor, optimization of the GTL process based on the developed reactor model, and dynamic simulation and optimal operating procedures for the micro-channel FT reaction system. The reactor models were validated against the real operation data. A distributed parameter model for micro-channel FT reactor was developed by using a new method, in which all the process and cooling channels are decomposed into a number of unit cells. Each neighboring process and cooling channel unit cells are coupled to set up material and energy balance equations, including heat-transfer equations for the entire reactor domain, which are then solved simultaneously. The model results were compared with the experimental data for a pilot-scale reactor described in the literature, and were found to be in good agreement. Several case studies were performed to see the effect of variables such as catalyst loading ratio, coolant flow rate, and channel layout on design of a cross-current type reactor with state-of-the-art Fischer–Tropsch catalyst. The cell-coupling model was then modified to consider more realistic type of flow configurations and flow distribution effect. Cell domain was re-defined for each flow configuration, and the realistic flow distribution effect was incorporated into the model by using results obtained from computational fluid dynamics (CFD). Several case studies were conducted to see the effect of flow configurations, flow distribution, and catalyst loading zones. It was observed that the geometry of cross-co-cross current was found to give the best performance among the designs considered. Optimal GTL process was suggested by conducting steady-state simulations where the developed micro-channel FT reactor model was implemented in the form of regressed artificial neural network (ANN) model. First, steady state model for a conventional GTL process was developed. Then, an optimization problem was formulated by defining objective function as the net profit. Design variables for this problem were the pressure and temperature of the FT reactor, split ratio for purge, and recycle flowrate to the FT reactor. Nelder-Mead algorithm was used to solve this derivative-free optimization problem. It can be said that by utilizing the reaction heat of the FT reactor, the reboiler duty for the CO2 separation was reduced, and the overall efficiency was increased. Optimal solution showed better economic performances over the base case design. A dynamic model for the FT reactor was developed. A partial differential equation for the 3D cell-coupling model was formulated and solved to obtain time dependent temperature profile in the entire reactor domain. Several case studies were performed to analyze dynamic behavior of the micro-channel reactor. Separate dynamic simulations were also conducted to suggest optimal start-up and shut-down procedure for the FT reactor system. Several scenarios were generated to analyze the thermal and hydrodynamic behvaior of the reactor. Optimal operating strategies for both start-up and shut-down of the reactor could be obtained. This work could contribute to desigining optimal GTL process, especially using a large-scale micro-channel Fischer-Tropsch reactor containing more than 1,000 process channels. The developed reactor model, steady-state model, and dynamic model could be utilized for designing and operation of the GTL system.CHAPTER 1 : Introduction 1 1.1. Research motivation 1 1.2. Research objectives 3 1.3. Outline of the thesis 4 CHAPTER 2 : Reactor model using cell-coupling method 5 2.1. Cross-current reactor model 5 2.1.1. Introduction 5 2.1.2. Model description 7 2.1.3. Model validation 17 2.1.4. Case studies 22 2.1.5. Conclusion 35 2.2. Improved reactor model 37 2.2.1. Introduction 37 2.2.2. Model construction 40 2.2.3. Model validation 50 2.2.4. Case studies 59 2.2.5. Conclusion 80 CHAPTER 3 : Optimization for GTL process 81 3.1. Introduction 81 3.2. Model description 82 3.2.1. Steady-state process model 82 3.2.2. Micro-channel reactor model 89 3.3. Optimization for the steady-state model 93 3.3.1. Cost model 93 3.3.2. Problem formulation 98 3.4. Results and discussion 102 3.5. Conclusion 105 CHAPTER 4 : Dynamic simulation for microchannel Fischer-Tropsch reactor 106 4.1. Introduction 106 4.2. Dynamic modeling for 3D reactor 108 4.2.1. Model description 108 4.2.2. Results and anlysis 110 4.3. Optimal operating strategies for FT reactor 115 4.3.1. Model description 115 4.3.2. Start-up 119 4.3.3. Shut-down 124 4.4. Conclusion 131 CHAPTER 5 : Concluding Remarks 132 5.1. Conclusions 132 5.2. Future works 133 Nomenclature 134 Literature cited 139 Abstract in Korean (요 약) 146Docto

Realistic adsorption geometries and binding affinities of metal nanoparticles onto the surface of carbon nanotubes

Adsorption geometries and binding affinities of metal nanoparticles onto carbon nanotubes (CNTs) are investigated through density-functional-theory calculations. Clusters of 13 metal atoms are used as models for metal nanoparticles. Palladium, platinum, and titanium particles strongly chemisorb to the CNT surface. Unlike the cases of atomic adsorptions the aluminum particle has the weakest binding affinity with the CNT. Aluminum or gold nanoparticles accumulated on the CNT develop the triangular bonding network of the metal surfaces in which the metal-carbon bond is not favored. This suggests that the CNT-Al interface is likely to have many voids and thus susceptible to oxidation damages.open10

Interpretable machine learning for prediction of clinical outcomes in acute ischemic stroke

Background and aimsPredicting the prognosis of acute ischemic stroke (AIS) is crucial in a clinical setting for establishing suitable treatment plans. This study aimed to develop and validate a machine learning (ML) model that predicts the functional outcome of AIS patients and provides interpretable insights.MethodsWe included AIS patients from a multicenter stroke registry in this prognostic study. ML-based methods were utilized to predict 3-month functional outcomes, which were categorized as either favorable [modified Rankin Scale (mRS) ≤ 2] or unfavorable (mRS ≥ 3). The SHapley Additive exPlanations (SHAP) method was employed to identify significant features and interpret their contributions to the predictions of the model.ResultsThe dataset comprised a derivation set of 3,687 patients and two external validation sets totaling 250 and 110 patients each. Among them, the number of unfavorable outcomes was 1,123 (30.4%) in the derivation set, and 93 (37.2%) and 32 (29.1%) in external sets A and B, respectively. Among the ML models used, the eXtreme Gradient Boosting model demonstrated the best performance. It achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.790 (95% CI: 0.775–0.806) on the internal test set and 0.791 (95% CI: 0.733–0.848) and 0.873 (95% CI: 0.798–0.948) on the two external test sets, respectively. The key features for predicting functional outcomes were the initial NIHSS, early neurologic deterioration (END), age, and white blood cell count. The END displayed noticeable interactions with several other features.ConclusionML algorithms demonstrated proficient prediction for the 3-month functional outcome in AIS patients. With the aid of the SHAP method, we can attain an in-depth understanding of how critical features contribute to model predictions and how changes in these features influence such predictions

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We propose a self-supervised learning method for long text documents based on contrastive learning. A key to our method is Shuffle and Divide (SaD), a simple text augmentation algorithm that sets up a pretext task required for contrastive updates to BERT-based document embedding. SaD splits a document into two sub-documents containing randomly shuffled words in the entire documents. The sub-documents are considered positive examples, leaving all other documents in the corpus as negatives. After SaD, we repeat the contrastive update and clustering phases until convergence. It is naturally a time-consuming, cumbersome task to label text documents, and our method can help alleviate human efforts, which are most expensive resources in AI. We have empirically evaluated our method by performing unsupervised text classification on the 20 Newsgroups, Reuters-21578, BBC, and BBCSport datasets. In particular, our method pushes the current state-of-the-art, SS-SB-MT, on 20 Newsgroups by 20.94% in accuracy. We also achieve the state-of-the-art performance on Reuters-21578 and exceptionally-high accuracy performances (over 95%) for unsupervised classification on the BBC and BBCSport datasets.Comment: Accepted at ICPR 202

ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision

The recent advances in representation learning inspire us to take on the challenging problem of unsupervised image classification tasks in a principled way. We propose ContraCluster, an unsupervised image classification method that combines clustering with the power of contrastive self-supervised learning. ContraCluster consists of three stages: (1) contrastive self-supervised pre-training (CPT), (2) contrastive prototype sampling (CPS), and (3) prototype-based semi-supervised fine-tuning (PB-SFT). CPS can select highly accurate, categorically prototypical images in an embedding space learned by contrastive learning. We use sampled prototypes as noisy labeled data to perform semi-supervised fine-tuning (PB-SFT), leveraging small prototypes and large unlabeled data to further enhance the accuracy. We demonstrate empirically that ContraCluster achieves new state-of-the-art results for standard benchmark datasets including CIFAR-10, STL-10, and ImageNet-10. For example, ContraCluster achieves about 90.8% accuracy for CIFAR-10, which outperforms DAC (52.2%), IIC (61.7%), and SCAN (87.6%) by a large margin. Without any labels, ContraCluster can achieve a 90.8% accuracy that is comparable to 95.8% by the best supervised counterpart.Comment: Accepted at ICPR 202

zkVoting : Zero-knowledge proof based coercion-resistant and E2E verifiable e-voting system

We introduce ${zkVoting}$, a coercion-resistant e-voting system that utilizes a fake keys approach based on a novel nullifiable commitment scheme. This scheme allows voters to receive both real and fake commitment keys from a registrar. Each ballot includes this commitment, but only the tallier can efficiently discern the fake ballots, simplifying the tally process to $\mathcal{O}(n)$ and ensuring coercion resistance. ${zkVoting}$ also preserves voter anonymity by ensuring each ballot conceals the voter\u27s identity. Additionally, by integrating zero-knowledge proofs, ${zkVoting}$ achieves end-to-end (E2E) verifiability. We formally prove its security and demonstrate its practicality for real-world applications, with a ballot casting time of 2.3 seconds and a tally time of 3.9 milliseconds per ballot

Entity-level Factual Adaptiveness of Fine-tuning based Abstractive Summarization Models

Abstractive summarization models often generate factually inconsistent content particularly when the parametric knowledge of the model conflicts with the knowledge in the input document. In this paper, we analyze the robustness of fine-tuning based summarization models to the knowledge conflict, which we call factual adaptiveness. We utilize pre-trained language models to construct evaluation sets and find that factual adaptiveness is not strongly correlated with factual consistency on original datasets. Furthermore, we introduce a controllable counterfactual data augmentation method where the degree of knowledge conflict within the augmented data can be adjustable. Our experimental results on two pre-trained language models (PEGASUS and BART) and two fine-tuning datasets (XSum and CNN/DailyMail) demonstrate that our method enhances factual adaptiveness while achieving factual consistency on original datasets on par with the contrastive learning baseline.Comment: EACL 202