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RISS 인기검색어
- 검색키워드
- 저자 : K. Rajesh Babu (검색결과 3 건)
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M. V. Ganeswara Rao,N. Ramanjaneyulu,Balamurali Pydi,Umamaheshwar Soma,K. Rajesh Babu,Satti Harichandra Prasad 한국전기전자재료학회 2023 Transactions on Electrical and Electronic Material Vol.24 No.6
The rapid advancement in nanoscale devices demands innovative gate dielectric materials to replace traditional Silicon dioxide. This paper investigates the electrical behavior and performance of a dual-gate FinFET employing different high-K gate dielectric materials (Silicon dioxide, Hafnium oxide, Titanium oxide) through ATLAS 2D simulation in 5 nm technology. We analyze how these high-K gate dielectric materials influence the device, focusing on performance enhancement. The study highlights various key performance parameters (ION, IOFF, gm, gds, RON, TF, EV, V IL, V IH, NML, NMH) and reveals a significant performance improvement with HfO2 dielectric material in the proposed Dual-Gate FinFET. Achieving impressive performance parameters ( ION : 21.59 mA, IOFF : 21 µA, Maximum net Electric field: 1221290 V/cm, g m(max) : 0.05187 S, gds(max) : 0.03462 S, RON(max) : 25.93 kΩ , TFmax: 5.02, G ainmax : 90.233, EVmax : 67.532 V, V IL : 0.21 V, V IH : 0.4 V, NML : 198 V, NMH : 600 V), this paper provides valuable insights for designing high-performance devices with HfO2 dielectric material.
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Effect of Sintering Temperature on the Micro Strain and Magnetic Properties of Ni-Zn Nanoferrites
D. Venkatesh,M. Siva Ram Prasad,B. Rajesh Babu,K. V. Ramesh,K. Trinath 한국자기학회 2015 Journal of Magnetics Vol.20 No.3
In this study, nanocrystalline ferrite powders with the composition Ni0.5Zn0.5Fe₂O₄ were prepared by the autocombustion method. The obtained powders were sintered at 800℃, 900℃ and 1,000℃ for 4 h in air atmosphere. The as-prepared and the sintered powders were characterized by X-ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, and magnetization studies. An increase in the crystallite size and a slight decrease in the lattice constant with sintering temperature were observed, whereas microstrain was observed to be negative for all the samples. Two significant absorption bands in the wave number range of the 400 cm<SUP>?1</SUP> to 600 cm<SUP>?1</SUP> have been observed in the FT-IR spectra for all samples which is the distinctive feature of the spinel ferrites. The force constants were found to vary with sintering temperature, suggesting a cation redistribution and modification in the unit cell of the spinel. The M-H loops indicate smaller coercivity, which is the typical nature of the soft ferrites. The observed variation in the saturation magnetization and coercivity with sintering temperature has been attributed to the role of surface, inhomogeneous cation distribution, and increase in the crystallite size.
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Prognostication of Climate Using Sliding Window Algorithm
D.V.N. Koteswara Rao,M.Anusha,P. Nagendra Babu,M. Divya Sri,N.Rajesh,K. Sandeep Kumar 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.4
Weather forecasting is the task of determining future state of the atmosphere. To predict the future’s weather condition, the variation in the conditions in past years must be utilized. The probability that the weather condition of the day in consideration will match the same day in previous year is very less. But the probability that it will match within the span of adjacent sixty days of previous year is very high. A Sliding window algorithm is emerging as a leading methodology for the application of weather prediction. So, the prediction is made based on sliding window algorithm. So, sixty days are considered for previous year a sliding window is selected of size equivalent to fifteen days. Every thirty days of sliding window is then matched with that of current year’s thirty days in consideration. The best matched window is made to participate in the process of predicting weather conditions. The month wise results are being computed for four months to check the accuracy. The experimental results demonstrate that the applied technique gives better predicted weather conditions are quite efficient with an average accuracy of 94.21%.
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