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논문 기본 정보

자료유형
학술저널
저자정보
G. Lakshmi Vara Prasad (Gitam University) Venkatagurunatham Naidu Kollu (Koneru Lakshmaiah Education Foundation) M. Sailaja (Prasad V Potluri Siddhartha Institute of Technology) S. Radhakrishnan (KKR & , KSR Institute of Technology & Sciences) K. Jagan Mohan (KKR & , KSR Institute of Technology & Sciences) A. Kishore Reddy (Andhra Engineering College) G. Rajesh Chandra (KKR & KSR Institute of Technology & Sciences)
저널정보
한국전기전자재료학회 Transactions on Electrical and Electronic Materials Transactions on Electrical and Electronic Materials Vol.25 No.1
발행연도
2024.2
수록면
89 - 97 (9page)
DOI
https://doi.org/10.1007/s42341-023-00487-z

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In this paper, we delve into the intriguing realm of Pseudo-morphic High Electron Mobility Transistors (pHEMTs) composed of InAs∕In0.3Al0.7As∕InSb∕In0.3Al0.7As layers, utilizing Silvaco-TCAD for simulation. Our focus centers on the assessment of RF and analog electrical characteristics, with a keen eye on the high-temperature eff ects. The influence of temperature on device performance is meticulously evaluated in comparison to a reference device operating at room temperature. Traditionally, the critical parameters such as threshold voltage ( Vth ), transconductance ( gm ), and Ion∕Ioff ratio have been calculated within the temperature range spanning from 300 K to 700 K. The primary pHEMT device in our study exhibits impressive attributes, featuring a drain current of 950 mA, a threshold voltage of -1.75 V, a high transconductance ( gm ) value of 650 mS/mm, an Ion∕Ioff ratio of 1 × 106 , a transition frequency ( ft ) soaring to 790 GHz, and a maximum frequency ( fmax ) reaching a staggering 1.4 THz. However, as we traverse the temperature spectrum, our findings unveil a compelling narrative. The impact of rising temperature is unequivocal, triggering a cascade of transformations within the device. Notably, as the temperature escalates, we observe a noticeable decrease in current, a reduction in transconductance ( gm ), and a diminishing Ion∕Ioff ratio. To unravel the intricacies of these temperature-induced effects, we introduce the infusion of Machine Learning (ML) into our analysis.

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