• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.560-561
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• 2022

본 논문은 Nanosheet FET(NSFET)와 FinFET의 소자 성능을 3차원 소자 시뮬레이션을 통하여 다양한 구조의 NSFET와 FinFET의 소자 시뮬레이션을 한다. NSFET와 FinFET의 전류-전압 특성을 시뮬레이션하였고, 그 전류-전압 특성으로부터 추출한 문턱전압, 문턱전압이하 기울기 등의 성능을 비교하였다. NSFET이 FinFET보다 전류-전압 특성에서 드레인 전류가 더 많이 흐르며 더 높은 문턱전압을 갖는다. 문턱전압이하 기울기는 NSFET와이 FinFET보다 더 가파른 기울기를 갖는다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.121-122
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• 2022

본 논문은 Nanosheet FET(NSFET)와 FinFET의 구조를 갖는 소자 성능을 조사하기 위해서 3차원 소자 시뮬레이터를 이용하여 시뮬레이션한 결과를 소개한다. NSFET와 FinFET의 채널 도핑 농도에 따른 전류-전압 특성을 시뮬레이션하였고, 그 전류-전압 특성으로부터 추출한 문턱전압, 문턱전압이하 기울기 등의 성능을 비교하였다. NSFET이 FinFET보다 채널 도핑 농도에 따른 전류-전압 특성에서 드레인 전류가 더 많이 흐르며 더 높은 문턱전압을 갖는다. 문턱전압이하 기울기는 NSFET가 FinFET보다 더 가파른 기울기를 갖는다.

• 한국전기전자재료학회논문지
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• 제35권1호
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• pp.50-57
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• 2022

Reliability of CMOS has been severed under aggressive device scaling. Conventional technologies such as lightly doped drain (LDD) and forming gas annealing (FGA) have been applied for better device reliability, but further advances are modest. Alternatively, electro-thermal annealing (ETA) which utilizes Joule heat produced by electrodes in a MOSFET, has been newly introduced for gate dielectric curing. However, concerns about mechanical stability during the electro-thermal annealing, have not been discussed, yet. In this context, this paper demonstrates the mechanical stability of nanosheet FET during the electro-thermal annealing. The effect of mechanical stresses during the electro-thermal annealing was investigated with respect to device design parameters.

• 반도체디스플레이기술학회지
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• 제22권4호
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• pp.136-141
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• 2023

The continuous shrinking of transistors in integrated circuits leads to difficulties in improving performance, resulting in the emerging transistors such as nanosheet field-effect transistors. In this paper, we propose a TCAD-machine learning framework of nanosheet FETs to model the current-voltage characteristics. Sentaurus TCAD simulations of nanosheet FETs are performed to obtain a large amount of device data. A machine learning model of I-V characteristics is trained using the multi-layer perceptron from these TCAD data. The weights and biases obtained from multi-layer perceptron are implemented in a PSPICE netlist to verify the accuracy of I-V and the DC transfer characteristics of a CMOS inverter. It is found that the proposed machine learning model is applicable to the prediction of nanosheet field-effect transistors device and circuit performance.

• Nanomaterials
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• 제12권22호
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• pp.4096-4107
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• 2022

In this work, a hybrid-phase transition field-effects-transistor (hyper-FET) integrated with phase-transition materials (PTM) and a multi-nanosheet FET (mNS-FET) at the 3 nm technology node were analyzed at the device and circuit level. Through this, a benchmark was performed for presenting device design guidelines and for using ultra-low-power applications. We present an optimization flow considering hyper-FET characteristics at the device and circuit level, and analyze hyper-FET performance according to the phase transition time (TT) and baseline-FET off-leakage current (I_OFF) variations of the PTM. As a result of inverter ring oscillator (INV RO) circuit analysis, the optimized hyper-FET increases speed by +8.74% and reduces power consumption by -16.55%, with IOFF = 5 nA of baseline-FET and PTM TT = 50 ps compared to the conventional mNS-FET in the ultra-low-power region. As a result of SRAM circuit analysis, the read static noise margin is improved by 43.9%, and static power is reduced by 58.6% in the near-threshold voltage region when the PTM is connected to the pull-down transistor source terminal of 6T SRAM for high density. This is achieved at 41% read current penalty.