Scaling behavior of ferroelectric FET with reduction in number of domains in ferroelectric layer

With the gate-length scaling, the number of domains in FeFET is reduced to a few or a single domain. In this paper, we investigate the effect of multi-domains versus few/single-domain behavior in FeFET. The abrupt polarization switching behavior of a single-domain is obtained by modifying the Preisach model in which the difference between saturation and remnant polarization (PsPr) is reduced. We show that for the same program/erase voltage, a two-times higher memory window can be achieved with single/few-domains FeFET than the multi-domain FeFET. Further, at fixed program/erase voltage, the scaling behavior shows improved variability due to increased polarization-induced vertical field with single-domain FeFET. We present an optimized device with a single-domain FeFET having a low operating voltage of ±2.4 V but with the same device performance that can be achieved for multi-domain FeFET having a higher operating voltage of ±5 V, which is highly promising for low power applications.

Compact model of retention characteristics of ferroelectric FinFET synapse with MFIS gate stack

In this paper, multiple-fin n- and p-channel HfZrO2 ferroelectric-FinFET devices are manufactured using a gate first process with post metalization annealing. The device transfer characteristics upon program and erase operations are measured and modeled. The drift in the transfer characteristics due to depolarization field and charge injection are captured using the shift in the threshold voltage along with time-dependent modeling of vertical field dependent mobility degradation parameters to develop a physical, computationally efficient, and accurate retention model for ferroelectric-FinFET devices. The modeled conductance is incorporated into deep neural network simulation platform CIMulator to analyze the role of conductance drift due to retention degradation, as well as the importance of the gap between high and low conductance states in improving the image recognition accuracy of neural networks.

Universal Protoplanetary Disk Size under Complete Nonideal Magnetohydrodynamics: The Interplay between Ion-neutral Friction, Hall Effect, and Ohmic Dissipation

The role of nonideal magnetohydrodynamics has been proven critical during the formation of protoplanetary disks, particularly in regulating their sizes. We provide a simple model to predict the disk size under the interplay among ambipolar diffusion, the Hall effect, and ohmic dissipation. The model predicts a small disk size of around 20 au that depends only sublinearly on disk parameters, for a wide range of initial conditions of subsolar mass and moderate magnetization. It is able to explain phenomena manifested in existing numerical simulations, including the bimodal disk behavior under parallel and antiparallel alignment between the rotation and magnetic field. In the parallel configuration, the disk size decreases and eventually disappears. In the antiparallel configuration, the disk has an outer partition (or pseudodisk), which is flat, shrinking, and short-lived, as well as an inner partition, which grows slowly with mass and is long-lived. Even with significant initial magnetization, the vertical field in the disk can only dominate at the early stage when the mass is low, and the toroidal field eventually dominates in all disks.

Real-Time Object Detection for LiDAR Based on LS-R-YOLOv4 Neural Network

Recently, self-driving cars became a big challenge in the automobile industry. After the DARPA challenge, which introduced the design of a self-driving system that can be classified as SAR Level 3 or higher levels, driven to focus on self-driving cars more. Later on, using these introduced design models, a lot of companies started to design self-driving cars. Various sensors, such as radar, high-resolution cameras, and LiDAR are important in self-driving cars to sense the surroundings. LiDAR acts as an eye of a self-driving vehicle, by offering 64 scanning channels, 26.9° vertical field view, and a high-precision 360° horizontal field view in real-time. The LiDAR sensor can provide 360° environmental depth information with a detection range of up to 120 meters. In addition, the left and right cameras can further assist in obtaining front image information. In this way, the surrounding environment model of the self-driving car can be accurately obtained, which is convenient for the self-driving algorithm to perform route planning. It is very important for self-driving to avoid the collision. LiDAR provides both horizontal and vertical field views and helps in avoiding collision. In an online website, the dataset provides different kinds of data like point cloud data and color images which helps this data to use for object recognition. In this paper, we used two types of publicly available datasets, namely, KITTI and PASCAL VOC. Firstly, the KITTI dataset provides in-depth data knowledge for the LiDAR segmentation (LS) of objects obtained through LiDAR point clouds. The performance of object segmentation through LiDAR cloud points is used to find the region of interest (ROI) on images. And later on, we trained the network with the PASCAL VOC dataset used for object detection by the YOLOv4 neural network. To evaluate, we used the region of interest image as input to YOLOv4. By using all these technologies, we can segment and detect objects. Our algorithm ultimately constructs a LiDAR point cloud at the same time; it also detects the image in real-time.