A perturbative approach to predict eye diagram degradation in differential interconnects subject to asymmetry and nonuniformity

This paper proposes a novel framework for the signal integrity (SI) analysis of differential interconnects affected by nonuniformity and geometrical asymmetry. The pertinent nonuniform transmission-line (TL) equations are solved in the frequency domain by means of a perturbation technique, which allows interpreting the resulting response degradation as a perturbation with respect to the response of a reference uniform differential line (DL) with averaged per-unit-length (p.u.l.) parameters. Following this interpretation, the problem is recast as a standard TL equation for the reference uniform line with additional equivalent distributed sources that account for the perturbative effect of asymmetric nonuniformity. This equivalent perturbation problem is solved iteratively in the frequency domain, and the corresponding time-domain behavior is obtained via inverse Fourier transform. Moreover, upon consideration that local perturbations negligibly impact on far-end differential mode (DM) quantities, the uniform DL model with averaged p.u.l. parameters is used for the SI performance evaluation of transmitted DM voltages in SPICE, showing that comparable results can be obtained while avoiding the cumbersome implementation of a nonuniform transmission line topology. The methodology is applied to the prediction of the eye diagram degradation for a 20 Gbps transmission through a microstrip DL subject to geometrical asymmetry and nonuniformity

Compensating mode conversion due to bend discontinuities through intentional trace asymmetry

In this letter, a comparative analysis is carried out between the mechanism of mode conversion in differential microstrip lines due to bend discontinuities on one side and trace asymmetry on the other side. With the help of equivalent modal circuits, a theoretical basis is provided for the idea to compensate the undesired common mode (CM), due to the presence of the bend, by intentionally designing asymmetric traces. As an application example, the proposed CM-reduction strategy is used in conjunction with another recently-presented wideband CM suppression filter for differential microstrip lines. It is shown that the proposed solution enhances the overall CM-reduction performance of the filter by some decibels, while preserving its transmission properties

Modeling of imbalance in differential lines targeted to SPICE simulation

partially_open5siIn this paper, a SPICE model representative for the mode conversion occurring in differential lines affected by imbalance either of the line cross-section and the terminal networks is developed. The model is based on the assumption of weak imbalance and allows approximate prediction of modal quantities, through separate modeling of the contributions due to line and termination imbalance by controlled sources with (possibly) frequency dependent gain. The proposed SPICE model is used to perform worst-case prediction of undesired modal voltages induced at line terminals by mode conversion.openGrassi, Flavia; Wu, Xinglong; Yang, Yuehong; Spadacini, Giordano; Pignari, Sergio A.Grassi, Flavia; Wu, Xinglong; Yang, Yuehong; Spadacini, Giordano; Pignari, SERGIO AMEDE

MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

Pretraining with large-scale 3D volumes has a potential for improving the segmentation performance on a target medical image dataset where the training images and annotations are limited. Due to the high cost of acquiring pixel-level segmentation annotations on the large-scale pretraining dataset, pretraining with unannotated images is highly desirable. In this work, we propose a novel self-supervised learning strategy named Volume Fusion (VF) for pretraining 3D segmentation models. It fuses several random patches from a foreground sub-volume to a background sub-volume based on a predefined set of discrete fusion coefficients, and forces the model to predict the fusion coefficient of each voxel, which is formulated as a self-supervised segmentation task without manual annotations. Additionally, we propose a novel network architecture based on parallel convolution and transformer blocks that is suitable to be transferred to different downstream segmentation tasks with various scales of organs and lesions. The proposed model was pretrained with 110k unannotated 3D CT volumes, and experiments with different downstream segmentation targets including head and neck organs, thoracic/abdominal organs showed that our pretrained model largely outperformed training from scratch and several state-of-the-art self-supervised training methods and segmentation models. The code and pretrained model are available at https://github.com/openmedlab/MIS-FM.Comment: 13 pages, 8 figure