dynamic switching
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2022 ◽  
Author(s):  
Shubham Sahay ◽  
Amol Gaidhane ◽  
Yogesh Singh Chauhan ◽  
Raghvendra Dangi ◽  
Amit Verma

<div>In this paper, we develop a Verilog-A implementable compact model for the dynamic switching of ferroelectric Fin-FETs (Fe-FinFETs) for asymmetric non-periodic input signals. We use the multi-domain Preisach Model to capture the saturated P-E loop of the ferroelectric capacitors. In addition to the saturation loop, we model the history dependent minor loop paths in the P-E by tracing input signals’ turning points. To capture the input signals’ turning points, we propose an R-C circuit based approach in this work. We calibrate our proposed model with the experimental data, and it accurately captures the history effect and minor loop paths of the ferroelectric capacitor. Furthermore, the elimination of storage of each turning point makes the proposed model computationally efficient compared with the previous implementations. We also demonstrate the unique electrical characteristics of Fe-FinFETs by integrating the developed compact model of Fe-Cap with the BSIM-CMG model of 7nm FinFET.</div>


2022 ◽  
Author(s):  
Shubham Sahay ◽  
Amol Gaidhane ◽  
Yogesh Singh Chauhan ◽  
Raghvendra Dangi ◽  
Amit Verma

<div>In this paper, we develop a Verilog-A implementable compact model for the dynamic switching of ferroelectric Fin-FETs (Fe-FinFETs) for asymmetric non-periodic input signals. We use the multi-domain Preisach Model to capture the saturated P-E loop of the ferroelectric capacitors. In addition to the saturation loop, we model the history dependent minor loop paths in the P-E by tracing input signals’ turning points. To capture the input signals’ turning points, we propose an R-C circuit based approach in this work. We calibrate our proposed model with the experimental data, and it accurately captures the history effect and minor loop paths of the ferroelectric capacitor. Furthermore, the elimination of storage of each turning point makes the proposed model computationally efficient compared with the previous implementations. We also demonstrate the unique electrical characteristics of Fe-FinFETs by integrating the developed compact model of Fe-Cap with the BSIM-CMG model of 7nm FinFET.</div>


2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yifan Hu ◽  
Peng Xun ◽  
Peidong Zhu ◽  
Wenjie Kang ◽  
Yinqiao Xiong ◽  
...  

Static characteristics of supervisory control and data acquisition (SCADA) system are often exploited to perform malicious activities on smart grids. Most of the time, the success of cyberattacks begins with the profiling of the target system and follows by the analysis of the limited resources. To alleviate the asymmetry between attack and defense, network-based moving target defense (MTD) techniques have been applied in the network system to defend against cyberattacks by constructing a dynamic attack surface to the adversary. In this paper, we propose a novel MTD technique based on adaptive forwarding path migration (AFPM) that focuses on improving the defense capability and optimizing the network performance of path mutation. Considering the transient problems present in path mutation caused by the dynamic switching of the forwarding path, we formalize the mutation constraints based on the satisfiability modulo theory (SMT) to select the mutation path. Considering the limited defense capability of path mutation owing to the traditional mutation selection mechanism, we design the mutation path generation algorithm based on the network security capacity matrix to obtain an optimal combination of mutation path and mutation period. Finally, we compare and analyze various cyber defense techniques used in the SCADA network and demonstrate experimentally that our MTD technique can prevent more than 92% of passive monitoring under specified conditions while ensuring the quality of service (QoS) to be almost the same as the static network.


2021 ◽  
Vol 2021 ◽  
pp. 1-3
Author(s):  
Yong Hu

DNA nanotechnology takes DNA molecule out of its biological context to build nanostructures that have entered the realm of robots and thus added a dimension to cyborg and bionic systems. Spurred by spring-like properties of DNA molecule, the assembled nanorobots can be tuned to enable restricted, mechanical motion by deliberate design. DNA nanorobots can be programmed with a combination of several unique features, such as tissue penetration, site-targeting, stimuli responsiveness, and cargo-loading, which makes them ideal candidates as biomedical robots for precision medicine. Even though DNA nanorobots are capable of detecting target molecule and determining cell fate via a variety of DNA-based interactions both in vitro and in vivo, major obstacles remain on the path to real-world applications of DNA nanorobots. Control over nanorobot’s stability, cargo loading and release, analyte binding, and dynamic switching both independently and simultaneously represents the most eminent challenge that biomedical DNA nanorobots currently face. Meanwhile, scaling up DNA nanorobots with low-cost under CMC and GMP standards represents other pertinent challenges regarding the clinical translation. Nevertheless, DNA nanorobots will undoubtedly be a powerful toolbox to improve human health once those remained challenges are addressed by using a scalable and cost-efficient method.


2021 ◽  
Vol 2005 (1) ◽  
pp. 012150
Author(s):  
Nanzhou Chen ◽  
Shan Hu ◽  
Wenhao Zhu ◽  
Fei Wang

Quantum ◽  
2021 ◽  
Vol 5 ◽  
pp. 469
Author(s):  
Ewout van den Berg

We describe an efficient implementation of Bayesian quantum phase estimation in the presence of noise and multiple eigenstates. The main contribution of this work is the dynamic switching between different representations of the phase distributions, namely truncated Fourier series and normal distributions. The Fourier-series representation has the advantage of being exact in many cases, but suffers from increasing complexity with each update of the prior. This necessitates truncation of the series, which eventually causes the distribution to become unstable. We derive bounds on the error in representing normal distributions with a truncated Fourier series, and use these to decide when to switch to the normal-distribution representation. This representation is much simpler, and was proposed in conjunction with rejection filtering for approximate Bayesian updates. We show that, in many cases, the update can be done exactly using analytic expressions, thereby greatly reducing the time complexity of the updates. Finally, when dealing with a superposition of several eigenstates, we need to estimate the relative weights. This can be formulated as a convex optimization problem, which we solve using a gradient-projection algorithm. By updating the weights at exponentially scaled iterations we greatly reduce the computational complexity without affecting the overall accuracy.


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