Optimization of ANN ‐based models and its EM co‐simulation for printed RF devices

2021 ◽  
Author(s):  
Shuai Yang ◽  
Ahmad Khusro ◽  
Weiwei Li ◽  
Mohammad Vaseem ◽  
Mohammad Hashmi ◽  
...  
Keyword(s):  
Rf Devices

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.

RF-Identity

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Chao Feng ◽  
Jie Xiong ◽  
Liqiong Chang ◽  
Fuwei Wang ◽  
Ju Wang ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Critical Role ◽  
Training Data ◽  
Identification Accuracy ◽  
Identification System ◽  
Frequency Bandwidth ◽  
Person Identification ◽  
Data Set ◽  
Rf Devices ◽  
Large Frequency

Person identification plays a critical role in a large range of applications. Recently, RF based person identification becomes a hot research topic due to the contact-free nature of RF sensing that is particularly appealing in current COVID-19 pandemic. However, existing systems still have multiple limitations: i) heavily rely on the gait patterns of users for identification; ii) require a large amount of data to train the model and also extensive retraining for new users and iii) require a large frequency bandwidth which is not available on most commodity RF devices for static person identification. This paper proposes RF-Identity, an RFID-based identification system to address the above limitations and the contribution is threefold. First, by integrating walking pattern features with unique body shape features (e.g., height), RF-Identity achieves a high accuracy in person identification. Second, RF-Identity develops a data augmentation scheme to expand the size of the training data set, thus reducing the human effort in data collection. Third, RF-Identity utilizes the tag diversity in spatial domain to identify static users without a need of large frequency bandwidth. Extensive experiments show an identification accuracy of 94.2% and 95.9% for 50 dynamic and static users, respectively.

Low-frequency noise conversion modeling in RF devices under forced nonlinear operation

2005 ◽  
Vol 16 (1) ◽  
pp. 4-12 ◽  
Author(s):  
Gabriele Conte ◽  
Francesco Bertazzi ◽  
Simona Donati Guerrieri ◽  
Fabrizio Bonani ◽  
Giovanni Ghione
Keyword(s):  
Low Frequency ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Rf Devices ◽  
Nonlinear Operation
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