• 제목/요약/키워드: Nanosheet-FET

검색결과 6건 처리시간 0.018초

Nanosheet FET와 FinFET의 전류-전압 특성 비교 (Comparison of Current-Voltage Characteristics of Nanosheet FET and FinFET)

  • 안은서;유윤섭
    • 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.

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Radiation effects on multi-channel Forksheet-FET and Nanosheet-FET considering the bottom dielectric isolation scheme

  • Gunhee Choi;Jongwook Jeon
    • Nuclear Engineering and Technology
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    • 제56권11호
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    • pp.4679-4687
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    • 2024
  • This study analyzes the single-event transient (SET) characteristics of alpha particles on multi-channel Forksheet-FET and Nanosheet-FET at the device and circuit levels through 3D TCAD simulations. The study investigates the differences in SET responses based on the energy and incident position of incoming alpha particles, considering the structural variances between Forksheet-FET and Nanosheet-FET, as well as the presence or absence of bottom dielectric isolation (BDI) in the fabrication process. Specifically, the introduction of BDI is observed to significantly suppress the voltage drop caused by 'unintended' current, as it can block the substantial electron-hole pairs (EHP) generated by injected alpha particles in the bulk substrate from reaching the FET terminals. Furthermore, it was confirmed that the size of abnormal current decreases as the energy of the injected alpha particle increases. Additionally, evaluating the response to SET based on the fundamental logic circuit, the CMOS inverter, revealed relatively small abnormal voltage drops for both Forksheet and Nanosheet when BDI was applied, confirming high immunity to radiation effects. Moreover, it can be observed that the application of BDI enhances reliability from a memory perspective by effectively suppressing voltage flips in the SRAM's cross-coupled latch circuit.

Nanosheet FET와 FinFET의 도핑 농도에 따른 전류-전압 특성 비교 (Comparison of Current-Voltage Characteristics by Doping Concentrations of Nanosheet FET and FinFET)

  • 안은서;유윤섭
    • 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.

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Nanosheet FETs에서의 효과적인 전열어닐링 수행을 위한 기계적 안정성에 대한 연구 (Investigation of Mechanical Stability of Nanosheet FETs During Electro-Thermal Annealing)

  • 왕동현;박준영
    • 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.

TCAD-머신러닝 기반 나노시트 FETs 컴팩트 모델링 (Compact Modeling for Nanosheet FET Based on TCAD-Machine Learning)

  • 송준혁;이운복;이종환
    • 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.

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Hyper-FET's Phase-Transition-Materials Design Guidelines for Ultra-Low Power Applications at 3 nm Technology Node

  • Hanggyo Jung;Jeesoo Chang;Changhyun Yoo;Jooyoung Oh;Sumin Choi;Juyeong Song;Jongwook Jeon
    • 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 (IOFF) 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.