Sep 22, 2021
[paper] Abstraction NBTI model
Aug 30, 2021
Generalized EKV Compact MOSFET Model
Aug 6, 2021
[paper] Model for Ultra-Scaled MoS2 MOSFET
Acknowledgement: This work is supported in part by the Natural Science Foundation of China under Grant 61704144, the Shenzhen Science and Technology Project under JCYJ20180305125340386, the General Research Fund (GRF) from Research Grant Council (RGC) of Hong Kong under Grant 16206219
Jul 12, 2021
[PhD] Cryogenic MOSFET Modeling
Présentée le 28 mai 2021
pour l’obtention du grade de Docteur ès Sciences par
Arnout Lodewijk M BECKERS
Acceptée sur proposition du jury:
Prof. E. Charbon, président du jury
Prof. C. Enz, directeur de thèse
Prof. B. Parvais, rapporteur
Prof. G. Ghibaudo, rapporteur
Dr J.-M. Sallese, rapporteur
Jun 8, 2021
[paper] MOSFET Threshold Voltage Extraction
Abstract: A MOSFET threshold voltage extraction method covering the entire range of drain-to-source voltage, from linear to saturation modes, is presented. Transconductance-to-current ratio is obtained from MOSFET transfer characteristics measured at low to high drain voltage. Based on the charge-based modeling approach, a near-constant value of threshold voltage is obtained over the whole range of drain voltage for ideal, long-channel MOSFETs. The method reveals a distinct increase of threshold voltage versus drain voltage for halo-implanted MOSFETs in the low drain voltage range. The method benefits from moderate inversion operation, where high-field effects, such as vertical field mobility reduction and series resistances, are minimal. The present method is applicable over the full range of drain voltage, is fully analytical, easy to be implemented, and provides more consistent results when compared to existing methods.
May 25, 2021
[papers] Aging and Device Reliability Compact Modeling
[1] N. Chatterjee, J. Ortega, I. Meric, P. Xiao and I. Tsameret, "Machine Learning On Transistor Aging Data: Test Time Reduction and Modeling for Novel Devices," 2021 IEEE International Reliability Physics Symposium (IRPS), 2021, pp. 1-9, doi: 10.1109/IRPS46558.2021.9405188.
Abstract: Accurately modeling the I-V characteristics and current degradation for transistors is central to predicting circuit end-of-life behavior. In this work, we propose a machine learning model to accurately model current degradation at various stress conditions and extend that to make nominal use-bias predictions. The model can be extended to track and predict any parametric change. We show an excellent agreement of the model with experimental results. Furthermore, we use a deep neural network to model the I-V characteristics of aged transistors over a wide drain and gate playback bias range and show an excellent agreement with experimental results. We show that the model is reliably able to interpolate and extrapolate demonstrating that it learns the underlying functional form of the data.
URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9405188&isnumber=9405088
[2] P. B. Vyas et al., "Reliability-Conscious MOSFET Compact Modeling with Focus on the Defect-Screening Effect of Hot-Carrier Injection," 2021 IEEE International Reliability Physics Symposium (IRPS), 2021, pp. 1-4, doi: 10.1109/IRPS46558.2021.9405197.
Abstract: Accurate prediction of device aging plays a vital role in the circuit design of advanced-node CMOS technologies. In particular, hot-carrier induced aging is so complicated that its modeling is often significantly simplified, with focus limited to digital circuits. We present here a novel reliability-aware compact modeling method that can accurately capture the full post-stress I-V characteristics of the MOSFET, taking into account the impact of drain depletion region on induced defects.
URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9405197&isnumber=9405088
[3] Z. Wu et al., "Physics-based device aging modelling framework for accurate circuit reliability assessment," 2021 IEEE International Reliability Physics Symposium (IRPS), 2021, pp. 1-6, doi: 10.1109/IRPS46558.2021.9405106.
Abstract: An analytical device aging modelling framework, ranging from microscopic degradation physics up to the aged I-V characteristics, is demonstrated. We first expand our reliability oriented I-V compact model, now including temperature and body-bias effects; second, we propose an analytical solution for channel carrier profiling which-compared to our previous work-circumvents the need of TCAD aid; third, through Poisson's equation, we convert the extracted carrier density profile into channel lateral and oxide electric fields; fourth, we represent the device as an equivalent ballistic MOSFETs chain to enable channel “slicing” and propagate local degradation into the aged I-V characteristics, without requiring computationally-intensive self-consistent calculations. The local degradation in each channel “slice” is calculated with physics-based reliability models (2-state NMP, SVE/MVE). The demonstrated aging modelling framework is verified against TCAD and validated across a broad range of VG/VD/T stress conditions in a scaled finFET technology.
URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9405106&isnumber=9405088
May 18, 2021
[paper] An Accurate Analytical Modeling of Contact Resistances in MOSFETs
National Research Nuclear University MEPHI, Moscow, Russia;
*Orel State University, Russia;
Apr 13, 2021
[papers] Compact Modeling
Apr 7, 2021
[papers] compact modeling
Feb 12, 2021
[paper] ACM) Model in VHDL-AMS
*Holymary Institute Of Technology And Science, Bogaram(V), Keesara (M), Hyderabad
Jan 5, 2021
[paper] Aged MOSFET and Its Compact Modeling
Nov 24, 2020
[paper] Compact Models for Sizing Based on ANN
2LogiqWorks Ltd. Sofia, Bulgaria
3Reutlingen University Reutlingen, Germany
Nov 19, 2020
[paper] Compact Model for Power MOSFET
National School of Applied Sciences of Safi, Cadi Ayyad University, Marrakech (MA)
Sep 29, 2020
[thesis] RF UTBB FDSOI MOSFET
Sep 8, 2020
[paper] RF Small-Signal Model for Four-Port Network MOSFETs
2Maxim Integrated, Chandler, AZ, USA.
3Klipsch School of Electrical and Computer Engineering, New Mexico State University, Las Cruces, NM, USA.
Acknowledgment: This work has been supported by PRODEP program from SEP (Secretariat of Public Education, Mexico) and Universidad Autonoma de Aguascalientes, Aguascalientes, Mexico.