TCAD-Enabled Machine Learning Defect Prediction to Accelerate Advanced Semiconductor Device Failure Analysis

Teo, Chek Sing; Low, Kain Lu; Narang, Vinod; Thean, Aaron

doi:10.1109/sispad.2019.8870440

Cited by 37 publications

(18 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, it has been proposed that the TCAD simulation based on well-calibrated parameters can be used to generate enough data for ML in variation and failure analysis [4] [5]. Ref.…”

mentioning

confidence: 99%

See 1 more Smart Citation

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

“…Recently, it has been proposed that the TCAD simulation based on well-calibrated parameters can be used to generate enough data for ML in variation and failure analysis [4] [5]. Ref.…”

mentioning

confidence: 99%

“…Moreover, physical quantities extraction (e.g. extraction of threshold voltage and sub-threshold slope in I-V characteristics) was required in the ML frameworks that were reported in [5] and [6], which limits their applicability.…”

mentioning

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

“…Therefore, in [6], we have proposed to use Technology Computer-Aided Design (TCAD) to generate defective and variation data for ML. A Similar idea was also proposed by another group in [7]. TCAD models are calibrated to experiment.…”

Section: Introductionmentioning

confidence: 94%

Improvement of TCAD Augmented Machine Learning Using Autoencoder for Semiconductor Variation Identification and Inverse Design

et al. 2020

View full text Add to dashboard Cite

A machine learning (ML) model by combing two autoencoders and one linear regression model is proposed to avoid overfitting and to improve the accuracy of Technology Computer-Aided Design (TCAD)-augmented ML for semiconductor structural variation identification and inverse design, without using domain expertise. TCAD-augmented ML utilizes TCAD simulations to generate sufficient data for ML model development when experimental data are inadequate. The ML model can then be used to identify semiconductor structural variation for given experimental electrical measurements. In this study, the variation of layer thicknesses in the p-i-n diode is used as a demonstration. An ML model is developed to predict the diode layer thicknesses based on a given Current-Voltage (IV) curve. Although the variations of interest can be incorporated easily in TCAD simulations to generate ML training data, the TCAD-augmented ML model generally is overfitted and cannot predict the variations in experiment well due to hidden variables which also alters the IV curves. We show that by using an autoencoder, this problem can be solved. To verify the effectiveness, another set of TCAD simulation data is generated with hidden variables (dopant concentration variation) to emulate experimental data. Testing on the second set of data shows that the proposed model can avoid overfitting and has up to 15 times improvement in accuracy in thickness prediction. Moreover, this model is used successfully to perform inverse design and can capture an underlying physics that cannot be described by a simple physical parameter.

show abstract

“…77,78 The other way is to utilize the TCAD data and use the learning algorithm to build the related formulation for extracting device performance merits. 47,79,80 Several attempts have been made using both the ways mentioned above to alleviate the need for heavy TCAD simulations. 24,38,43,80,81 Another problem that is often faced is the time and computational complexity in the stage of dataset generation, preprocessing, predictive analysis.…”

Section: Machine Learning Algorithm and Comparative Analysismentioning

confidence: 99%

Multilayer perceptron–random forest based hybrid machine learning–neural network model for GaN high electron mobility transistor's parameter estimations

Mishra

Raut

Sehra

et al. 2022

Int J RF Mic Comp-Aid Eng

View full text Add to dashboard Cite

TCAD-Enabled Machine Learning Defect Prediction to Accelerate Advanced Semiconductor Device Failure Analysis

Cited by 37 publications

References 4 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

Improvement of TCAD Augmented Machine Learning Using Autoencoder for Semiconductor Variation Identification and Inverse Design

Multilayer perceptron–random forest based hybrid machine learning–neural network model for GaN high electron mobility transistor's parameter estimations

Contact Info

Product

Resources

About