Modeling of chemical mechanical polishing at multiple scales

Chemical Mechanical Polishing (CMP) has grown rapidly during the past decade as part of mainstream processing method in submicron integrated circuit manufacturing because of its global or near-global planarization ability. However, CMP process is influenced by many factors and is poorly understood. It makes process control and optimization very difficult. This study focuses on the modeling and simulation to facilitate better understanding and better control of the CMP process. The thesis outlines the modeling of CMP process in three scales: particle scale for material removal mechanism, wafer scale for within wafer nonuniformity issues and feature scale for dishing and erosion in metal CMP.;At the particle scale, material removal mechanism is assumed to be due to local plastic deformation of wafer surface at the abrasive - wafer interface. Pad is assumed to deform like a beam to obtain an approximate force partition between abrasives and direct wafer-pad contact. A mechanistic material removal model is derived that delineates the influence of abrasive (shape, size and concentration), pad (rigidity) and process parameters (pressure and relative velocity) on the material removal rate (MRR).;Wafer scale model is based on the solution of indentation of elastic half space by a rigid frictionless polynomial punch. The elastic solution is derived through potential theory and complex analysis method. It is valid for any polynomial punch with integer power or non-integer power. The load-displacement relationship is also derived and the conditions for unbonded or bonded contact are obtained from the boundary condition at punch edge. The corresponding viscoelastic solution is obtained through Laplace transform and elastic-viscoelastic analogy. The elastic solution is used to explain the edge effect. The elastic analytical solution is first verified against numerical results from Finite Element Method (FEM) simulation. It shows wafer curvature, indentation depth and load will influence the interface pressure distribution throughout the wafer surface and it introduces parameters control as a potential avenue for completely eliminating the within wafer nonumiformity. Viscoelastic solution is used to explain within wafer nonuniformity, i.e., edge effect and wafer to wafer nonuniformity, i.e., removal rate decay for unconditioned pad. The relationships among wafer-pad interface pressure, wafer shape and wafer loading condition are also investigated.;Feature scale model for dishing and erosion is based on Preston\u27s relationship for material removal and constant downforce. It shows dishing will reach a limit and is governed by polishing conditions (overpolishing, pressure, velocity), slurry (selectivity), pad character istics (pad stiffness and bending ability), as well as wafer surface feature topography (pattern density, linewidth and pitch). This model is also valid for step height reduction when the same surface material is polished.;Due to process complexity and coupling of various parameters, more fundamental research needs to be carried out and carefully designed experiments need to be done to verify the models. Recommendations for future research work is presented at the end

Numerical simulation on air distribution of a tennis hall in winter and evaluation on indoor thermal environment

Supplying air with ball spout air diffusers is a common air-conditioning system for air distribution in large space stadiums. When supplying hot air with ball spout diffusers in winter, the phenomenon of hot jet upturning may appear, so the design should consider adjusting the spout angle so as to control the rising airflow. The purpose of the paper is to predict and optimize the air distribution of a tennis hall in winter for the purpose of guiding the design and regulation of air-conditioning system. Based on the optimal scheme of summer conditions, using computational fluid dynamics (CFD) technique, the air distribution and indoor thermal environment of a tennis hall in winter were numerically simulated. Two conditions were considered discharging air with spouts downwards with a 30 degree slope and discharging air horizontally. Indoor thermal environment was evaluated from two case studies including the protection of the movement of the ball and thermal comfort of the human body, and consequently, the optimal design was then proposed. The results can provide some guidance for air distribution design and spout regulation in winter conditions of air-conditioning systems in similar tennis halls

Elevated serum Activin A in chronic obstructive pulmonary disease with skeletal muscle wasting

OBJECTIVE: Muscle wasting contributes to the reduced quality of life and increased mortality in chronic obstructive pulmonary disease (COPD). Muscle atrophy in mice with cachexia was caused by Activin A binding to ActRIIB. The role of circulating Activin A leading to muscle atrophy in COPD remains elusive. METHODS: In the present study, we evaluated the relationship between serum levels of Activin A and skeletal muscle wasting in COPD patients. The expression levels of serum Activin A were measured in 78 stable COPD patients and in 60 healthy controls via ELISA, which was also used to determine the expression of circulating TNF-a levels. Total skeletal muscle mass (SMM) was calculated according to a validated formula by age and anthropometric measurements. The fat-free mass index (FFMI) was determined as the fat-free mass (FFM) corrected for body surface area. RESULTS: Compared to the healthy controls, COPD patients had upregulated Activin A expression. The elevated levels of Activin A were correlated with TNF-a expression, while total SMM and FFMI were significantly decreased in COPD patients. Furthermore, serum Activin A expression in COPD patients was negatively associated with both FFMI and BMI. CONCLUSION: The above results showed an association between increased circulating Activin A in COPD patients and the presence of muscle atrophy. Given our previous knowledge, we speculate that Activin A contributes to skeletal muscle wasting in COPD

Spin Accumulation in a Quantum Wire with Rashba Spin-Orbit Coupling

We investigate theoretically the spin accumulation in a Rashba spin-orbit coupling quantum wire. Using the scattering matrix approach within the effective free-electron approximation, we have demonstrated the three components of spin polarization. It is found that by a few numerical examples, the two peaks for the out-of-plane spin accumulation 〈Sz〉 shift to the edges of quantum wire with the increase of propagation modes. The period of intrinsic oscillations 〈Sx/y〉 inversely proportions to the Rashba SOC strength. This effect may be used to differentiate the intrinsic spin accumulation from the extrinsic one

Morphology-Enhanced CAM-Guided SAM for weakly supervised Breast Lesion Segmentation

Breast cancer diagnosis challenges both patients and clinicians, with early detection being crucial for effective treatment. Ultrasound imaging plays a key role in this, but its utility is hampered by the need for precise lesion segmentation-a task that is both time-consuming and labor-intensive. To address these challenges, we propose a new framework: a morphology-enhanced, Class Activation Map (CAM)-guided model, which is optimized using a computer vision foundation model known as SAM. This innovative framework is specifically designed for weakly supervised lesion segmentation in early-stage breast ultrasound images. Our approach uniquely leverages image-level annotations, which removes the requirement for detailed pixel-level annotation. Initially, we perform a preliminary segmentation using breast lesion morphology knowledge. Following this, we accurately localize lesions by extracting semantic information through a CAM-based heatmap. These two elements are then fused together, serving as a prompt to guide the SAM in performing refined segmentation. Subsequently, post-processing techniques are employed to rectify topological errors made by the SAM. Our method not only simplifies the segmentation process but also attains accuracy comparable to supervised learning methods that rely on pixel-level annotation. Our framework achieves a Dice score of 74.39% on the test set, demonstrating compareable performance with supervised learning methods. Additionally, it outperforms a supervised learning model, in terms of the Hausdorff distance, scoring 24.27 compared to Deeplabv3+'s 32.22. These experimental results showcase its feasibility and superior performance in integrating weakly supervised learning with SAM. The code is made available at: https://github.com/YueXin18/MorSeg-CAM-SAM

Diagnosis after Zooming in: A Multi-label Classification Model by Imitating Doctor Reading Habits to Diagnose Brain Diseases

International audiencePurpose: Computed tomography (CT) has the advantages of being low cost and noninvasive and is a primary diagnostic method for brain diseases. However, it is a challenge for junior radiologists to diagnose CT images accurately and comprehensively. It is necessary to build a system that can help doctors diagnose and provide an explanation of the predictions. Despite the success of deep learning algorithms in the field of medical image analysis, the task of brain disease classification still faces challenges: Researchers lack attention to complex manual labeling requirements and the incompleteness of prediction explanations. More importantly, most studies only measure the performance of the algorithm, but do not measure the effectiveness of the algorithm in the actual diagnosis of doctors. Methods: In this paper, we propose a model called DrCT2 that can detect brain diseases without using image-level labels and provide a more comprehensive explanation at both the slice and sequence levels. This model achieves reliable performance by imitating human expert reading habits: targeted scaling of primary images from the full slice scans and observation of suspicious lesions for diagnosis. We evaluated our model on two open-access data sets: CQ500 and the RSNA Intracranial Hemorrhage Detection Challenge. In addition, we defined three tasks to comprehensively evaluate model interpretability by measuring whether the algorithm can select key images with lesions. To verify the algorithm from the perspective of practical application, three junior radiologists were invited to participate in the experiments, comparing the effects before and after human-computer cooperation in different aspects. Results: The method achieved F1-scores of 0.9370 on CQ500 and 0.8700 on the RSNA data set. The results show that our model has good interpretability under the premise of good performance. Human radiologist evaluation experiments have proven that our model can effectively improve the accuracy of the diagnosis and improve efficiency. Conclusions: We proposed a model that can simultaneously detect multiple brain diseases.The report generated by the model can assist doctors in avoiding missed diagnoses, and it has good clinical application value

An unsupervised domain adaptation brain CT segmentation method across image modalities and diseases

International audienceComputed tomography (CT) is the primary diagnostic tool for brain diseases. To determine the appropriate treatment plan, it is necessary to ascertain the patient's bleeding volume. Automatic segmentation algorithms for hemorrhagic lesions can significantly improve efficiency and avoid treatment delays. However, for deep supervised learning algorithms, a large amount of labeled training data is usually required, making them difficult to apply clinically. In this study, we propose an unsupervised domain adaptation method that is an unsupervised domain adaptation segmentation model that can be trained across modalities and diseases. We call it AMD-DAS for brain CT hemorrhage segmentation tasks. This circumvents the heavy data labeling task by converting the source domain data (MRI with glioma) to our task's required data (CT with Intraparenchymal hemorrhage (IPH)). Our model implements a two-stage domain adaptation process to achieve this objective. In the first stage, we train a pseudo-CT image synthesis network using the CycleGAN architecture through a matching mechanism and domain adaptation approach. In the second stage, we use the model trained in the first stage to synthesize the pseudo-CT images. We use the pseudo-CT with source domain labels and real CT images to train a domain-adaptation segmentation model. Our method exhibits a better performance than the basic one-stage domain adaptation segmentation method (+11.55 Dice score) and achieves an 86.93 Dice score in the IPH unsupervised segmentation task. Our model can be trained without using a ground-truth label, therefore increasing its application potential. Our implementation is publicly available at https://github.com/GuanghuiFU/AMD-DAS-Brain-CT-Segmentation

Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospects

The complexity of psychological principles underscore a significant societal challenge, given the vast social implications of psychological problems. Bridging the gap between understanding these principles and their actual clinical and real-world applications demands rigorous exploration and adept implementation. In recent times, the swift advancement of highly adaptive and reusable artificial intelligence (AI) models has emerged as a promising way to unlock unprecedented capabilities in the realm of psychology. This paper emphasizes the importance of performance validation for these large-scale AI models, emphasizing the need to offer a comprehensive assessment of their verification from diverse perspectives. Moreover, we review the cutting-edge advancements and practical implementations of these expansive models in psychology, highlighting pivotal work spanning areas such as social media analytics, clinical nursing insights, vigilant community monitoring, and the nuanced exploration of psychological theories. Based on our review, we project an acceleration in the progress of psychological fields, driven by these large-scale AI models. These future generalist AI models harbor the potential to substantially curtail labor costs and alleviate social stress. However, this forward momentum will not be without its set of challenges, especially when considering the paradigm changes and upgrades required for medical instrumentation and related applications