Integration and Performance Improvements of Silicon Nanocrystal Memories

2004 ◽  
Vol 830 ◽  
Author(s):  
T. Hiramoto ◽  
I. Kim ◽  
M. Saitoh ◽  
K. Yanagidaira

ABSTRACTThe silicon nanocrystal memory, that is one of the most promising devices for future non-volatile memory, is extensively investigated by experiments and simulation. The silicon nanocrystal memory cells are successfully integrated using the state-of-the-art 0.13 μm DRAM technology. The mechanism of the two-bit-per-cell operation, that is one of the unique features of silicon nanocrystal memory, is investigated and it is shown that the degree of DIBL determines the read scheme of the two-bit-per-cell operation. Moreover, the dependences of memory characteristics on device structures are examined by fabrication and measurements and it is found that the ultra-thin-body SOI and double-gate structures have better memory characteristics.

MRS Bulletin ◽  
1997 ◽  
Vol 22 (10) ◽  
pp. 49-54 ◽  
Author(s):  
E. Todd Ryan ◽  
Andrew J. McKerrow ◽  
Jihperng Leu ◽  
Paul S. Ho

Continuing improvement in device density and performance has significantly affected the dimensions and complexity of the wiring structure for on-chip interconnects. These enhancements have led to a reduction in the wiring pitch and an increase in the number of wiring levels to fulfill demands for density and performance improvements. As device dimensions shrink to less than 0.25 μm, the propagation delay, crosstalk noise, and power dissipation due to resistance-capacitance (RC) coupling become significant. Accordingly the interconnect delay now constitutes a major fraction of the total delay limiting the overall chip performance. Equally important is the processing complexity due to an increase in the number of wiring levels. This inevitably drives cost up by lowering the manufacturing yield due to an increase in defects and processing complexity.To address these problems, new materials for use as metal lines and interlayer dielectrics (ILDs) and alternative architectures have surfaced to replace the current Al(Cu)/SiO2 interconnect technology. These alternative architectures will require the introduction of low-dielectric-constant k materials as the interlayer dielectrics and/or low-resistivity conductors such as copper. The electrical and thermomechanical properties of SiO2 are ideal for ILD applications, and a change to material with different properties has important process-integration implications. To facilitate the choice of an alternative ILD, it is necessary to establish general criterion for evaluating thin-film properties of candidate low-k materials, which can be later correlated with process-integration problems.


Author(s):  
Inzamam Mashood Nasir ◽  
Muhammad Rashid ◽  
Jamal Hussain Shah ◽  
Muhammad Sharif ◽  
Muhammad Yahiya Haider Awan ◽  
...  

Background: Breast cancer is considered as the most perilous sickness among females worldwide and the ratio of new cases is expanding yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images. Objective: Most of these systems have either used traditional handcrafted features or deep features which had a lot of noise and redundancy, which ultimately decrease the performance of the system. Methods: A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and deep features to classify the breast cancer images. HOG and LBP features are serially fused with pretrained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification performance of proposed method. Results: The method concentrates on histopathological images to classify the breast cancer. The performance is compared with state-of-the-art techniques, where an overall patient-level accuracy of 97.2% and image-level accuracy of 96.7% is recorded. Conclusion: The proposed hybrid method achieves the best performance as compared to previous methods and it can be used for the intelligent healthcare systems and early breast cancer detection.


2019 ◽  
Vol 13 (2) ◽  
pp. 14-31
Author(s):  
Mamdouh Alenezi ◽  
Muhammad Usama ◽  
Khaled Almustafa ◽  
Waheed Iqbal ◽  
Muhammad Ali Raza ◽  
...  

NoSQL-based databases are attractive to store and manage big data mainly due to high scalability and data modeling flexibility. However, security in NoSQL-based databases is weak which raises concerns for users. Specifically, security of data at rest is a high concern for the users deployed their NoSQL-based solutions on the cloud because unauthorized access to the servers will expose the data easily. There have been some efforts to enable encryption for data at rest for NoSQL databases. However, existing solutions do not support secure query processing, and data communication over the Internet and performance of the proposed solutions are also not good. In this article, the authors address NoSQL data at rest security concern by introducing a system which is capable to dynamically encrypt/decrypt data, support secure query processing, and seamlessly integrate with any NoSQL- based database. The proposed solution is based on a combination of chaotic encryption and Order Preserving Encryption (OPE). The experimental evaluation showed excellent results when integrated the solution with MongoDB and compared with the state-of-the-art existing work.


Author(s):  
Xiaomo Jiang ◽  
Craig Foster

Gas turbine simple or combined cycle plants are built and operated with higher availability, reliability, and performance in order to provide the customer with sufficient operating revenues and reduced fuel costs meanwhile enhancing customer dispatch competitiveness. A tremendous amount of operational data is usually collected from the everyday operation of a power plant. It has become an increasingly important but challenging issue about how to turn this data into knowledge and further solutions via developing advanced state-of-the-art analytics. This paper presents an integrated system and methodology to pursue this purpose by automating multi-level, multi-paradigm, multi-facet performance monitoring and anomaly detection for heavy duty gas turbines. The system provides an intelligent platform to drive site-specific performance improvements, mitigate outage risk, rationalize operational pattern, and enhance maintenance schedule and service offerings via taking appropriate proactive actions. In addition, the paper also presents the components in the system, including data sensing, hardware, and operational anomaly detection, expertise proactive act of company, site specific degradation assessment, and water wash effectiveness monitoring and analytics. As demonstrated in two examples, this remote performance monitoring aims to improve equipment efficiency by converting data into knowledge and solutions in order to drive value for customers including lowering operating fuel cost and increasing customer power sales and life cycle value.


AIHA Journal ◽  
2003 ◽  
Vol 64 (5) ◽  
pp. 660-667 ◽  
Author(s):  
Katharyn A. Grant ◽  
John G. Garland ◽  
Todd C. Joachim ◽  
Andrew Wallen ◽  
Twyla Vital

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