Inverse modeling of MOSFETs using I-V characteristics in the subthreshold region

Lee, Z.K.; McIlrath, Michael B.; Antoniadis, D.A.

doi:10.1109/iedm.1997.650475

Cited by 8 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This will not be the case in experiment because there are other variations and noises which will affect the I-V's. This is an example of an inverse design problem [26]- [29] which can also be used for defect analysis and reverse engineering.…”

Section: A With Physical Quantities Extractionmentioning

confidence: 99%

TCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis With Experimental Demonstration

Wong

Xiao

Wang

et al. 2020

IEEE J. Electron Devices Soc.

View full text Add to dashboard Cite

This work, for the first time, experimentally demonstrates a TCAD-Machine Learning (TCAD-ML) framework to assist the analysis of device-to-device variation and operating (ambient) temperature without the need of physical quantities extraction. The ML algorithm used in this work is the Principal Component Analysis (PCA) followed by third order polynomial regression. After calibrated to limited 'expensive' experimental data, 'low cost' TCAD simulation is used to generate a large amount of device data to train the ML model. The ML was then used to identify the root cause of device variation and operating temperature from any given experimental current-voltage (I-V) characteristics. We applied this framework to study the ultra-wide-bandgap gallium oxide (Ga2O3) Schottky barrier diode (SBD), an emerging device technology that holds great promise for temperature sensing, RF, and power applications in harsh environments. After calibration, over 150,000 electrothermal TCAD simulations are performed with random variation of physical parameters (anode effective work function, drift layer doping, and drift layer thickness) and operating temperature. An ML model is trained using these TCAD data and we found 1,000-10,000 TCAD data can train an accurate machine. We show that without physical quantities extraction, performing PCA is essential for the TCAD trained ML model to be applicable to analyze experimental characteristics. The physical parameters and temperatures predicted by the ML model show good agreement with experimental analysis. Our TCAD-ML framework shows great promise to accelerate the development of new device technologies with a significantly more efficient process of material and device experimentation.

show abstract

Section: A With Physical Quantities Extractionmentioning

confidence: 99%

TCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis With Experimental Demonstration

Wong

Xiao

Wang

et al. 2020

IEEE J. Electron Devices Soc.

View full text Add to dashboard Cite

show abstract

“…Now, substituting the Wdm in (12) into (7), we have , 1 1 (14) where M N N 2 (14) is not a GP compatible constraint; as a result, in this work, we relax (14) into two inequalities. First, we change the operator from "=" to " " of (14), and take square of (14) for both sides, we will have: , 1 1 1…”

Section: Integration Of 2d Poisson Equationmentioning

confidence: 99%

“…The inequality (17) is a GP compatible constraint since the right-hand side is a monomial function and the left-hand side is a posynomial. Consequently, we change the operator from "=" to " " of (14), and based on the same procedure from (15) to (17), we have:…”

Section: Integration Of 2d Poisson Equationmentioning

confidence: 99%

“…governs the device performance, affects the short-channel effect (SCE) [1][2][3][4][5][6][7], and regulates the random-dopant-fluctuation-induced threshold voltage fluctuation (RDF-induced Vt) [8][9][10][11][12][13]15] significantly. However, various doping-profile designs strongly rely on device engineering experiences in process/device simulation and fabrication experiment, such as retrograde doping profile method [6], methodology of inverse modeling using I-V characteristics [14], simulation-based evolutionary technique [15] and regression method [16]. Unfortunately, applications of these procedures to find suitable doping profiles are cases sensitive and may not always be guaranteed in global sense.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Optimal Channel Doping Profile of Two-Dimensional Metal-Oxide-Semiconductor Field-Effect Transistors via Geometric Programming

Chen

2015

JASSE

View full text Add to dashboard Cite

In this study, we theoretically optimize a two-dimensional (2D) channel doping profile of metal-oxide-semiconductor field-effect transistors (MOSFETs) with given current voltage (I-V) characteristics by using a geometric programming (GP) technique. Inverse modeling of channel doping profile for device characteristics with simultaneously considering the short-channel effect (SCE) and random-dopant-fluctuation-induced threshold voltage fluctuation (RDF-induced Vt) is advanced. The formulated model of doping profile is a GP problem which can be transformed into a convex optimization problem and solved globally and efficiently. Constrains of I-V characteristics with including the RDF-induced Vt are included to optimize desired doping profiles. The optimization methodology is applied for 45-nm MOSFET devices and the results are validated with 2D numerical device simulation. This approach provides an alternative way to design doping profile for various technologies of MOSFETs.

show abstract

“…This method provides higher speed because no process simulation steps are required. The demand from the semiconductor industry for such methods has increased rapidly in the past few years, offering the possibility to fit simulated doping profiles to measured device data (inverse modeling [26], [27]).…”

Section: B Optimization Of Analytical Doping Profilesmentioning

confidence: 99%

Integrated optimization capabilities in the VISTA technology CAD framework

Plasun

Stockinger

Selberherr

1998

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

View full text Add to dashboard Cite

Inverse modeling of MOSFETs using I-V characteristics in the subthreshold region

Cited by 8 publications

References 6 publications

TCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis With Experimental Demonstration

TCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis With Experimental Demonstration

Optimal Channel Doping Profile of Two-Dimensional Metal-Oxide-Semiconductor Field-Effect Transistors via Geometric Programming

Integrated optimization capabilities in the VISTA technology CAD framework

Contact Info

Product

Resources

About