An advanced MOSFET design approach and a calibration methodology using inverse modeling that accurately predicts device characteristics

Das, Aloke Kumar; Newmark, D.; Clejan, I.; Foisy, M.; Sharma, Megha; Venkatesan, S.; Veeraraghavan, S.; Misra, Veena; Gadepally, B.; Parrillo, L.C.

doi:10.1109/iedm.1997.650476

Cited by 8 publications

(6 citation statements)

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“…Finally, our framework may be considered as one kind of inverse design. Compared to other inverse design using TCAD [25]- [28], our framework requires no physical quantities extraction and no complex optimizer (only simple 3 rd order polynomial and PCA). The TCAD-based inverse design has been proposed for many years but it is still far away from a wide industrial adoption, probably due to the need for too much domain expertise in both data processing/selection and optimizer optimization.…”

Section: Discussionmentioning

confidence: 99%

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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.

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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.

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Section: Discussionmentioning

confidence: 99%

“…Note that, even though physical quantities extraction is required in this case, it is intentionally minimized. It is important to avoid too much human intervention as in traditional inverse designs reported in [25]- [28]. 3 rd order polynomials are then generated from I and I for machine learning.…”

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.

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“…10 Using I -V characteristics, especially in the subthreshold regime, and capacitance characteristics of metal-oxide-semiconductor field effect transistors ͑MOS-FETs͒ of different channel lengths, a number of recent articles [11][12][13] have attempted to extract the two-dimensional ͑2D͒ doping distribution in the device using ''inverse modeling.'' Other than detecting a reduction in drive current, which might be caused by a number of other effects, I -V data alone are not able to measure either the lateral motion of the junction or its gradient.…”

Section: B Overlap Of Gate and Extension Junctionmentioning

confidence: 99%

Design and integration considerations for end-of-the roadmap ultrashallow junctions

Osburn

Yee

et al. 2000

Journal of Vacuum Science &Amp; Technology B: Microelectronics and Nanometer Structures Processing, Measurement, and Phenomena

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Device simulations and response surface analysis have been used to quantify the trade-offs and issues encountered in designing ultrashallow junctions for the 250-50 nm generations of complimentary metal-oxide-semiconductor ultralarge scale integration technology. The design of contacting and extension junctions is performed to optimize short channel effects, performance, and reliability, while meeting the National Technology Roadmap for Semiconductors off-state leakage specifications. A maxima in saturated drive current is observed for an intermediate extension junction depth ͑ϳ20 nm for 100 nm technology͒: shallower junctions lead to higher series resistance, and deeper junctions result in more severe short channel effects. The gate-to-junction overlap required to preserve drive current was seen to depend on junction abruptness. For a perfectly abrupt junction, it is not necessary for the gate to overlap the junction. Performance depends on many parameters, including: overlap of gate to extension junction, junction capacitance, and parasitic series resistance, which depends on the doping gradient at the junction ͑spreading resistance͒, the extension series resistance, and the contact resistance. Extraction of these parameters using I -V or C -V measurements can potentially lead to erroneous conclusions about lateral junction excursion and abruptness.

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“…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.

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An advanced MOSFET design approach and a calibration methodology using inverse modeling that accurately predicts device characteristics

Cited by 8 publications

References 0 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

Design and integration considerations for end-of-the roadmap ultrashallow junctions

Integrated optimization capabilities in the VISTA technology CAD framework

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