Practical inverse modeling with SIESTA

Sträßer, Rudolf; Plasun, R.; Selberherr, S.

doi:10.1109/sispad.1999.799268

Cited by 5 publications

(3 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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“…In order to perform our experiments, we used the simulation environment SIESTA [11], [12] which features a sophisticated job farming facility. To choose among all available hosts the so-called guess load is used (2) This is an estimation of the actual load which is computed by superimposing the system load ( ), the number of jobs already running ( ), and the number of recently finished jobs ( ).…”

Section: B Optimization Frameworkmentioning

confidence: 99%

A Study on Global and Local Optimization Techniques for TCAD Analysis Tasks

Binder

Heitzinger

Selberherr

2004

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

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Abstract-We evaluate optimization techniques to reduce the necessary user interaction for inverse modeling applications as they are used in the technology computer-aided design field. Four optimization strategies are compared. Two well-known global optimization methods, simulated annealing and genetic optimization, a local gradient-based optimization strategy, and a combination of a local and a global method. We rate the applicability of each method in terms of the minimal achievable target value for a given number of simulation runs and in terms of the fastest convergence. A brief overview over the three used optimization algorithms is given. The optimization framework that is used to distribute the workload over a cluster of workstations is described. The actual comparison is achieved by means of an inverse modeling application that is performed for various settings of the optimization algorithms. All presented optimization algorithms are capable of evaluating several targets in parallel. The best optimization strategy that is found is used in the calibration of a model for silicon self-interstitial cluster formation and dissolution.

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Optimization for TCAD Purposes Using Bernstein Polynomials

Heitzinger

Selberherr

2001

Simulation of Semiconductor Processes and Devices 2001

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Practical inverse modeling with SIESTA

Cited by 5 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

A Study on Global and Local Optimization Techniques for TCAD Analysis Tasks

Optimization for TCAD Purposes Using Bernstein Polynomials

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