Liquid packaging for MEMS sensors and RF devices

2004 ◽  
Author(s):  
P.A. Stupar ◽  
R.L. Borwick ◽  
J. DeNatale
Keyword(s):  
Mems Sensors ◽  
Rf Devices

Seismic monitoring network based on MEMS sensors

2019 ◽  
Vol 32 (3-4) ◽  
pp. 179-185
Author(s):  
Zhen-xuan Zou ◽  
◽  
Ming Zhang ◽  
Xu-dong He ◽  
Sheng-fa Lin ◽  
...  
Keyword(s):  
Monitoring Network ◽  
Seismic Monitoring ◽  
Mems Sensors

Functionalized MEMS Sensors for Capacity-Based Residual Life Indicators

2012 ◽  
Author(s):  
Peter J. Hesketh ◽  
Alex Robinson ◽  
Mark Allendorf
Keyword(s):  
Residual Life ◽  
Mems Sensors

scholarly journals MEMS sensors for sport engineer applications

2021 ◽  
Vol 1038 (1) ◽  
pp. 012056
Author(s):  
C Russo ◽  
F Mocera ◽  
A Somà
Keyword(s):  
Mems Sensors ◽  
Sport Engineer

scholarly journals Coverage Layout Design Rules and Insertion Utilities for CMP-Related Processes

2020 ◽  
Vol 11 (1) ◽  
pp. 2
Author(s):  
Eitan N. Shauly ◽  
Sagee Rosenthal
Keyword(s):  
Integrated Circuit ◽  
Thermal Effects ◽  
Process Integration ◽  
Proximity Effects ◽  
Published Data ◽  
Metal Gate ◽  
Comprehensive Description ◽  
Advanced Technologies ◽  
High K ◽  
Rf Devices

The continuous scaling needed for higher density and better performance has introduced some new challenges to the planarity processes. This has resulted in new definitions of the layout coverage rules developed by the foundry and provided to the designers. In advanced technologies, the set of rules considers both the global and the local coverage of the front-end-of line (FEOL) dielectric layers, to the back-end-of-line (BEOL) Cu layers and Al layers, to support high-k/Metal Gate process integration. For advance technologies, a new set of rules for dummy feature insertion was developed by the integrated circuit (IC) manufacturers in order to fulfill coverage limits. New models and utilities for fill insertion were developed, taking into consideration the design coverage, thermal effects, sensitive signal line, critical analog and RF devices like inductors, and double patterning requirements, among others. To minimize proximity effects, cell insertion was also introduced. This review is based on published data from leading IC manufacturers with a careful integration of new experimental data accumulated by the authors. We aim to present a typical foundry perspective. The review provides a detailed description of the chemical mechanical polishing (CMP) process and the coverage dependency, followed by a comprehensive description of coverage rules needed for dielectric, poly, and Cu layers used in advanced technologies. Coverage rules verification data are then presented. RF-related aspects of some rules, like the size and the distance of dummy features from inductors, are discussed with additional design-for-manufacturing layout recommendations as developed by the industry.

scholarly journals Identification and authentication for wireless transmission security based on RF-DNA fingerprint

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xueli Wang ◽  
Yufeng Zhang ◽  
Hongxin Zhang ◽  
Xiaofeng Wei ◽  
Guangyuan Wang
Keyword(s):  
Radio Frequency ◽  
Classification Accuracy ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Wireless Transmission ◽  
Dna Fingerprint ◽  
Support Vector ◽  
Multiple Discriminant Analysis ◽  
Security Issue ◽  
Rf Devices

Abstract For wireless transmission, radio-frequency device anti-cloning has become a major security issue. Radio-frequency distinct native attribute (RF-DNA) fingerprint is a developing technology to find the difference among RF devices and identify them. Comparing with previous research, (1) this paper proposed that mean (μ) feature should be added into RF-DNA fingerprint. Thus, totally four statistics (mean, standard deviation, skewness, and kurtosis) were calculated on instantaneous amplitude, phase, and frequency generated by Hilbert transform. (2) We first proposed using the logistic regression (LR) and support vector machine (SVM) to recognize such extracted fingerprint at different signal-to-noise ratio (SNR) environment. We compared their performance with traditional multiple discriminant analysis (MDA). (3) In addition, this paper also proposed to extract three sub-features (amplitude, phase, and frequency) separately to recognize extracted fingerprint under MDA. In order to make our results more universal, additive white Gaussian noise was adopted to simulate the real environment. The results show that (1) mean feature conducts an improvement in the classification accuracy, especially in low SNR environment. (2) MDA and SVM could successfully identify these RF devices, and the classification accuracy could reach 94%. Although the classification accuracy of LR is 89.2%, it could get the probability of each class. After adding a different noise, the recognition accuracy is more than 80% when SNR≥5 dB using MDA or SVM. (3) Frequency feature has more discriminant information. Phase and amplitude play an auxiliary but also pivotal role in classification recognition.

Detection and Recognition of RF Devices Using Support Vector Machine

2013 ◽  
Vol 5 (4) ◽  
pp. 13-20
Author(s):  
Shikhar P. Acharya ◽  
Ivan G. Guardiola
Keyword(s):  
Support Vector Machine ◽  
Radio Frequency ◽  
Experimental Results ◽  
Support Vector ◽  
Noise Band ◽  
Detection And Identification ◽  
Electromagnetic Emissions ◽  
Rf Devices ◽  
Unintended Electromagnetic Emissions ◽  
Detection And Recognition

Radio Frequency (RF) devices produce some amount of Unintended Electromagnetic Emissions (UEEs). UEEs are generally unique to a device and can be used as a signature for the purpose of detection and identification. The problem with UEEs is that they are very low in power and are often buried deep inside the noise band. The research herein provides the application of Support Vector Machine (SVM) for detection and identification of RF devices using their UEEs. Experimental Results shows that SVM can detect RF devices within the noise band, and can also identify RF devices using their UEEs.

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