This paper proposes a novel neural network architecture combining convolutional and transposed convolutional neural networks to accurately and efficiently modelS-parameter of interconnects for 3D integration. The network incorporates physical consistency constraints, specifically causality and passivity, into its design to ensure the physical effectiveness of the output. The transposed convolutional network serves as a sub-network to map the relationship between the geometrical parameters andS-parameter for sub-structures. Then, theS-parameters of individual sub-structures are cascaded for dealing with a complex structure composed of sub-structures. A coupling neural network, with causality and passivity constraints, is developed to map the coarse cascadedS-parameters to the fine accurateS-parameters. With the help of this high-dimensional space mapping, a small amount of electromagnetic simulation data of complex interconnect structures is sufficient to learn the relationship between cascaded and realS-parameters. To ensure the completeness of the training set distribution when training CONN on small datasets, a sensitivity analysis-based training set screening method is proposed to enhance the training performance of CONN. The proposed algorithm is demonstrated in two different assemble structure applications. The results highlight the effectiveness, flexibility and versatility of the proposed architecture in modeling complex structures with small costly simulation data while maintaining accuracy and physical consistency.

As an important technology in high-speed systems, equalizer (EQ) is used to mitigate inter-symbol interference (ISI) caused by inconsistent attenuation of high and low frequencies. The difficulty of signal integrity improvement increases the complexity of EQ design, making the existing algorithms inefficient in high-dimensional searching and constraint processing. In this article, a local multi-constraint modeling-Bayesian optimization (BO) with region partitioning is proposed, aiming to provide a general optimization solution for high-dimensional multi-constraint EQs and improve convergence accuracy and efficiency. The constraint filtering mechanism is used to exclude areas that violate simulation-independent constraints. Local modeling and region partitioning techniques complement each other, taking into account both the local accuracy of the model and the global search performance of the algorithm. The multi-constraint modeling strategy allows simulation-dependent constraints to be pre-judged through the surrogate model, overcoming the shortcomings of the traditional solution of adding the penalty term to the target value, which makes it difficult to balance the weights and can only judge the constraints after simulation, thereby reducing the waste of computing resources caused by simulating data that violates the constraints. The proposed algorithm is applied to EQ optimization in a 16 Gbps high-bandwidth memory channel and a 64 Gbps differential peripheral component interconnect express channel, respectively. The algorithm is developed based on PyTorch, and the eye diagrams are obtained using Keysight ADS software. Two applications are conducted on computer with Intel Core i5-13500 processor and 32 GB RAM. By utilizing the region partitioning and constraint filtering techniques, the actual number of simulations in the optimization can be significantly reduced. The experimental results demonstrate that the proposed algorithm has significant shorter computing time than traditional BO and genetic algorithm, implying its practical application potential for dealing with high-dimensional multi-constraint problems.

Microfluidic Impedance Cytometry (MIC) is an advanced approach for single-cell analysis, harnessing microfluidic technology and impedance-based principles, particularly applicable in cancer diagnostics based on circulating tumor cells (CTCs). However, only relying on one dimensional information from electrical sensing poses challenges when detecting smaller CTCs that exhibit comparable sizes to white blood cells. To address this issue, we propose a microfluidic impedance cytometer featuring custom-designed circuits, electrodes, and a microfluidic chip with constriction channel. This system concurrently extracts both mechanical and electrical properties from processed electrical signals, overcoming the obstacle posed by the intrinsic link between impedance signals and cell sizes. Our system was tested with A549 lung cancer cells, white blood cells, and red blood cells, demonstrating the ability to differentiate cells of similar sizes within the blood sample and accurate cell couting capabilities. This approach shows promise for early cancer detection and monitoring treatment efficacy.

Automated segmentation of pathological fluid regions is crucial for digital diagnosis and individualized therapy under optical coherence tomography (OCT) images. Nonetheless, methods that rely on enormous annotations impede their clinical applications, as pixel-wise fine-grained labels are extraordinarily costly and require the expertise of ophthalmology. Though there exist works based on consistency-based and pseudo-label-based methods for reducing annotation reliance, the underutilization of consistency mechanisms and ignorance of fluid anatomical structures result in sub-optimal segmentation performance. In this work, we proposed a novel AMSC-Net devoted to semi-supervised fluid segmentation and achieves a 73.95% Dice score with 5% labeled data. Concretely, we developed a Heterogeneous Architecture Consistency (HAC) strategy based on our dual decoders enjoying different inductive biases. Moreover, we invented a Multi-label Semantic Consistency Loss (MSC-Loss) module in hierarchical semantic features and an Anatomy Contour Consistency Loss (ACC-Loss) module integrating anatomical constraint. These modules complementarily reinforce the quality of pseudo labels and boost semi-supervised training for robust segmentation results. To verify the superiority and bedside potential of our proposed AMSC-Net, we collected a large-scale fluid segmentation dataset composed of 22517 OCT images. Extensive quantitative and qualitative experiments validated the efficacy of our AMSC-Net with multiple novel techniques. Also, experimental results in a public fluid segmentation dataset demonstrated that our method achieves state-of-the-art performance. Code will be available at: https://github.com/ZeroOneGame/S4_Fluid_OCT.

Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results will hamper timely follow-up which is crucial to the patients’ treatment outcome. Nowadays, the evaluation is performed in a manual manner, which is time-consuming, subjective, and error-prone. In this article, we aim to automate this process by leveraging the advances in computer vision and artificial intelligence, to provide an objective and accurate method for root canal therapy result assessment. A novel anatomy-guided multi-branch Transformer (AGMB-Transformer) network is proposed, which first extracts a set of anatomy features and then uses them to guide a multi-branch Transformer network for evaluation. Specifically, we design a polynomial curve fitting segmentation strategy with the help of landmark detection to extract the anatomy features. Moreover, a branch fusion module and a multi-branch structure including our progressive Transformer and Group Multi-Head Self-Attention (GMHSA) are designed to focus on both global and local features for an accurate diagnosis. To facilitate the research, we have collected a large-scale root canal therapy evaluation dataset with 245 root canal therapy X-ray images, and the experiment results show that our AGMB-Transformer can improve the diagnosis accuracy from 57.96% to 90.20% compared with the baseline network. The proposed AGMB-Transformer can achieve a highly accurate evaluation of root canal therapy. To our best knowledge, our work is the first to perform automatic root canal therapy evaluation and has important clinical value to reduce the workload of endodontists.

Pneumothorax is a common pulmonary disease that can lead to dyspnea and can be life-threatening. X-ray examination is the main means to diagnose this disease. Computer-aided diagnosis of pneumothorax on chest X-ray, as a prerequisite for a timely cure, has been widely studied, but it is still not satisfactory to achieve highly accurate results. In this paper, an image classification algorithm based on the deep convolutional neural network (DCNN) is proposed for high-resolution medical image analysis of pneumothorax X-rays, which features a Network In Network (NIN) for cleaning the data, random histogram equalization data augmentation processing, and a DCNN. The experimental results indicate that the proposed method can effectively increase the correct diagnosis rate of pneumothorax, and the Area under Curve (AUC) of the test verified in the experiment is 0.9844 on ZJU-2 test data and 0.9906 on the ChestX-ray14, respectively. In addition, a large number of atmospheric pleura samples are visualized and analyzed based on the experimental results and in-depth learning characteristics of the algorithm. The analysis results verify the validity of feature extraction for the network. Combined with the results of these two aspects, the proposed X-ray image processing algorithm can effectively improve the classification accuracy of pneumothorax photographs.

Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability when handling COVID-19. However, classifying COVID-19 from pneumonia cases using CXR image is a difficult task because of shared spatial characteristics, high feature variation and contrast diversity between cases. Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models. To address these challenges, Multiscale Attention Guided deep network with Soft Distance regularization (MAG-SD) is proposed to automatically classify COVID-19 from pneumonia CXR images. In MAG-SD, MA-Net is used to produce prediction vector and attention from multiscale feature maps. To improve the robustness of trained model and relieve the shortage of training data, attention guided augmentations along with a soft distance regularization are posed, which aims at generating meaningful augmentations and reduce noise. Our multiscale attention model achieves better classification performance on our pneumonia CXR image dataset. Plentiful experiments are proposed for MAG-SD which demonstrates its unique advantage in pneumonia classification over cutting-edge models. The code is available at https://github.com/JasonLeeGHub/MAG-SD.

This paper proposes an efficient PSP-based model for cross-coupled metal-oxide-semiconductor field-effect transistors (MOSFETs) with optimized layout in the voltage controlled oscillator (VCO). The model employs a PSP charge model to characterize the bias-dependent extrinsic capacitance instead of numerical functions with strong non-linearity. The simulation convergence is greatly improved by this method. An original scheme is developed to extract the parameters of the PSP charge model based on S -parameters measurement. The interconnection parasitics of the cross-coupled MOSFETs are modeled based on vector fitting. The model is verified with an LC VCO design, and exhibits excellent convergence during simulation. The results show improvements as high as 60.5% and 61.8% in simulation efficiency and accuracy, respectively, indicating that the proposed model better characterizes optimized cross-coupled MOSFETs in advanced radio frequency (RF) circuit design.

A literal circuit with a three-track-output structure is presented based on resonant tunneling diodes (RTDs). It can be transformed conveniently into a single-track-output structure according to the definition and properties of the literal operation. A ternary resonant tunneling JK flip-flop is created based on the RTD literal circuit and the module-3 operation, and the JK flip-flop also has two optional types of output structure. The design of the ternary RTD JK flip-flop is verified by simulation. The RTD literal circuit is the key design component for achieving various types of multi-valued logic (MVL) flip-flops. It can be converted into ternary D and JK flip-flops, and the ternary JK flip-flop can also be converted simply and conveniently into ternary D and ternary T flip-flops when the input signals satisfy certain logical relationships. All these types of flip-flops can be realized using the traditional Karnaugh maps combined with the literal and module-3 operations. This approach offers a novel design method for MVL resonant tunneling flip-flop circuits.

The problems existing in the binary logic system and the advantages of multiple-valued logic (MVL) are introduced. A literal circuit with three-track-output structure is created based on resonant tunneling diodes (RTDs) and it has the most basic memory function. A ternary RTD D flip-flop with pre-set and pre-reset functions is also designed, the key module of which is the RTD literal circuit. Two types of output structure of the ternary RTD D flip-flop are optional: one is three-track and the other is single-track; these two structures can be transformed conveniently by merely adding tri-valued RTD NAND, NOR, and inverter units after the three-track output. The design is verified by simulation. Ternary flip-flop consists of an RTD literal circuit and it not only is easy to understand and implement but also provides a solution for the algebraic interface between the multiple-valued logic and the binary logic. The method can also be used for design of other types of multiple-valued RTD flip-flop circuits.

A new compact model for the substrate network of RF MOSFETs with deep n-well (DNW) implantation is presented. A novel test structure proposed in is employed to directly access the characteristics of the substrate in two-port measurements for the extraction of substrate network components. A method is developed to analytically extract the parameters for the substrate network from two-port measurements. The methodology is verified and validated by the excellent match between the measured and simulated output admittances for a 64-finger DNW n-MOSFET in common-source configuration.

The paper describes the evolution of the design of a motivational social visualization. The visualization shows the contributions of users to an online community to encourage social comparison and more participation. The newest design overcomes shortcomings in the previous two, by using more attractive appearance of the graphic elements in the visualization, better clustering algorithm and by giving up the largely unused in the previous design user customization options. The visualization integrates more information in one view, and uses an improved user clustering approach for representing graphically their different levels of contribution. A case study of the new design with a group of 32 students taking a class on Ethics and Computer Science is presented. The results show that the visualization had a significant effect on participation with respect to two activities (logging into the community and rating resources).