Recognizing Spatiotemporal Features by a Neuromorphic Network with Highly Reliable Ferroelectric Capacitors on Epitaxial GeSn Film

2021 ◽  
Author(s):  
Hao-Kai Peng ◽  
Yu-Kai Huang ◽  
Chuan-Pu Chou ◽  
Yung-Hsien Wu
Keyword(s):  
Spatiotemporal Features ◽  
Ferroelectric Capacitors

scholarly journals Multimodal Imaging with NanoGd Reveals Spatiotemporal Features of Neuroinflammation after Experimental Stroke

2021 ◽  
pp. 2101433
Author(s):  
Violaine Hubert ◽  
Ines Hristovska ◽  
Szilvia Karpati ◽  
Sarah Benkeder ◽  
Arindam Dey ◽  
...  
Keyword(s):  
Multimodal Imaging ◽  
Experimental Stroke ◽  
Spatiotemporal Features

Role of the Electrode Morphology, Thickness and Orientation in the Ferroelectric Performance of Epitaxial Pb(Zr,Ti)O3 Thin Films

2000 ◽  
Vol 655 ◽  
Author(s):  
Cesar Guerrero ◽  
Florencio Sánchez ◽  
José Roldán ◽  
Frank Güell ◽  
María V. García-Cuenca
Keyword(s):  
Electrode Materials ◽  
Stabilized Zirconia ◽  
Large Thickness ◽  
Low Dielectric ◽  
Thin Film Capacitors ◽  
Pzt Thin Film ◽  
Ferroelectric Layer ◽  
Ferroelectric Capacitors ◽  
Lattice Matched

AbstractA comparison of pulsed laser deposited PbZr0.53Ti0.47O3 (PZT) thin film capacitors with SrRuO3 (SRO) and LaNiO3 (LNO) electrodes on (001) yttria-stabilized zirconia (YSZ) and lattice matched (001) LaAlO3 substrates is presented. Both electrode materials allow for the formation of ferroelectric capacitors with large remnant polarization (20-30 μC/cm2) and negligible fatigue, although slight differences arise regarding the promotion of either the rhombohedral or tetragonal phases of PZT. Far more crucial seems to be the tendency of SrRuO3 to develop a rougher surface at either small (<30 nm) or large thickness (>100 nm), and on YSZ substrates. In those cases a highly defective and possibly low dielectric interface forms between the electrode and the ferroelectric layer, resulting in greatly degraded ferroelectric performance. LaNiO3 is free from these limitations except for the cracks forming at very large thickness (>300 nm), and therefore appears as a more versatile electrode material.

Selective spatiotemporal features learning for dynamic gesture recognition

2021 ◽  
Vol 169 ◽  
pp. 114499
Author(s):  
Xianlun Tang ◽  
Zhenfu Yan ◽  
Jiangping Peng ◽  
Bohui Hao ◽  
Huiming Wang ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Spatiotemporal Features ◽  
Dynamic Gesture Recognition

scholarly journals A 1T2C FeCAP-Based In-Situ Bitwise X(N)OR Logic Operation with Two-Step Write-Back Circuit for Accelerating Compute-In-Memory

Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 385
Author(s):  
Qiao Wang ◽  
Donglin Zhang ◽  
Yulin Zhao ◽  
Chao Liu ◽  
Qiao Hu ◽  
...  
Keyword(s):  
Nonvolatile Memory ◽  
High Reliability ◽  
Charge Sharing ◽  
Operation Speed ◽  
Sharing Function ◽  
Logic Operations ◽  
A Charge ◽  
Process Compatibility ◽  
Ferroelectric Capacitors

Ferroelectric capacitors (FeCAPs) with high process compatibility, high reliability, ultra-low programming current and fast operation speed are promising candidates to traditional volatile and nonvolatile memory. In addition, they have great potential in the fields of storage, computing, and memory logic. Nevertheless, effective methods to realize logic and memory in FeCAP devices are still lacking. This study proposes a 1T2C FeCAP-based in situ bitwise X(N)OR logic based on a charge-sharing function. First, using the 1T2C structure and a two-step write-back circuit, the nondestructive reading is realized with less complexity than the previous work. Second, a method of two-line activation is used during the operation of X(N)OR. The verification results show that the speed, area and power consumption of the proposed 1T2C FeCAP-based bitwise logic operations are significantly improved.

Negative capacitance in epitaxial ferroelectric capacitors evidenced by dynamic dielectric characterization

2021 ◽  
Vol 26 ◽  
pp. 102076
Author(s):  
Georgia Andra Boni ◽  
Cristina Chirila ◽  
Lucian Dragos Filip ◽  
Ioana Pintilie ◽  
Lucian Pintilie
Keyword(s):  
Negative Capacitance ◽  
Dielectric Characterization ◽  
Ferroelectric Capacitors

scholarly journals Two Stage Continuous Gesture Recognition Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 534
Author(s):  
Huogen Wang
Keyword(s):  
Gesture Recognition ◽  
Large Scale ◽  
Short Term Memory ◽  
Short Term ◽  
Hand Motion ◽  
Spatiotemporal Features ◽  
Spatiotemporal Information ◽  
Video Frames ◽  
Depth Sequences

The paper proposes an effective continuous gesture recognition method, which includes two modules: segmentation and recognition. In the segmentation module, the video frames are divided into gesture frames and transitional frames by using the information of hand motion and appearance, and continuous gesture sequences are segmented into isolated sequences. In the recognition module, our method exploits the spatiotemporal information embedded in RGB and depth sequences. For the RGB modality, our method adopts Convolutional Long Short-Term Memory Networks to learn long-term spatiotemporal features from short-term spatiotemporal features obtained from a 3D convolutional neural network. For the depth modality, our method converts a sequence into Dynamic Images and Motion Dynamic Images through weighted rank pooling and feed them into Convolutional Neural Networks, respectively. Our method has been evaluated on both ChaLearn LAP Large-scale Continuous Gesture Dataset and Montalbano Gesture Dataset and achieved state-of-the-art performance.

Video-based Indoor Human Gait Recognition Using Depth Imaging and Hidden Markov Model: A Smart System for Smart Home

2010 ◽  
Vol 20 (1) ◽  
pp. 120-128 ◽  
Author(s):  
Md. Zia Uddin ◽  
Tae-Seong Kim ◽  
Jeong Tai Kim
Keyword(s):  
Hidden Markov ◽  
Gait Recognition ◽  
Smart Homes ◽  
The Elderly ◽  
Recognition System ◽  
Home Healthcare ◽  
Human Gait ◽  
Depth Imaging ◽  
Smart System ◽  
Spatiotemporal Features

Smart homes that are capable of home healthcare and e-Health services are receiving much attention due to their potential for better care of the elderly and disabled in an indoor environment. Recently the Center for Sustainable Healthy Buildings at Kyung Hee University has developed a novel indoor human activity recognition methodology based on depth imaging of a user’s activities. This system utilizes Independent Component Analysis to extract spatiotemporal features from a series of depth silhouettes of various activities. To recognise the activities from the spatiotemporal features, trained Hidden Markov Models of the activities would be used. In this study, this technique has been extended to recognise human gaits (including normal and abnormal). Since this system could be of great significance for the caring of the elderly, to promote and preserve their health and independence, the gait recognition system would be considered a primary function of the smart system for smart homes. The indoor gait recognition system is trained to detect abnormal gait patterns and generate warnings. The system works in real-time and is aimed to be installed at smart homes. This paper provides the information for further development of the system for their application in the future.

Barrier performance of ultrathin Ni–Ti film for integrating ferroelectric capacitors on Si

2007 ◽  
Vol 91 (14) ◽  
pp. 142908 ◽  
Author(s):  
B. T. Liu ◽  
X. B. Yan ◽  
X. Zhang ◽  
C. S. Cheng ◽  
F. Li ◽  
...  
Keyword(s):  
Barrier Performance ◽  
Ferroelectric Capacitors
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