Powernet: SOI Lateral Power Device Breakdown Prediction With Deep Neural Networks

Chen, Jing; Alawieh, Mohamed Baker; Lin, Yibo; Zhang, Maolin; Zhang, Jun; Guo, Yufeng; Pan, David Z.

doi:10.1109/access.2020.2970966

Cited by 43 publications

(25 citation statements)

References 29 publications

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“…[6] proposed to use ML to replace TCAD simulation for device variation analysis and Ref. [7] used ML to replace TCAD simulation to for power device breakdown prediction. However, none of these frameworks has been verified with experimental data, which are usually non-ideal due to equipment limitation, extra variables, and measurement noise.…”

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.

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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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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.

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“…We use a normalization method to compress the structural parameters to (0, 1), and the process can eliminate the effect of unit and scale differences between input parameters in order to treat each class of input parameters equally, thereby increasing the prediction accuracy and efficiency of the ANN. Similar approaches to data pre-processing have been reported in papers related to machine learning [12,13].…”

Section: Methodsmentioning

confidence: 68%

“…Recently, machine learning techniques for predicting the electrical characteristic parameters of the semiconductor device have been booming due to their ability to learn the relationship between structural parameters and characteristics efficiently [8][9][10][11][12][13]. However, most work is limited to providing only one characteristic parameter, such as the threshold voltage of a junctionless nanowire transistor [11] or the breakdown voltage of a lateral power device [12]. As for those that can output multiple characteristics, the characteristic parameters are extracted from the current-voltage (I-V) curve and capacitance-voltage (C-V) curve, which still relies on human-intensive operation [13].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Static Characteristic Parameters of an Insulated Gate Bipolar Transistor Using Artificial Neural Network

et al. 2021

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Breakdown voltage (BV), on-state voltage (Von), static latch-up voltage (Vlu), static latch-up current density (Jlu), and threshold voltage (Vth), etc., are critical static characteristic parameters of an IGBT for researchers. Von and Vth can characterize the conduction capability of the device, while BV, Vlu, and Jlu can help designers analyze the safe operating area (SOA) of the device and its reliability. In this paper, we propose a multi-layer artificial neural network (ANN) framework to predict these characteristic parameters. The proposed scheme can accurately fit the relationship between structural parameters and static characteristic parameters. Given the structural parameters of the device, characteristic parameters can be generated accurately and efficiently. Compared with technology computer-aided design (TCAD) simulation, the average errors of our scheme for each characteristic parameter are within 8%, especially for BV and Vth, while the errors are controlled within 1%, and the evaluation speed is improved more than 107 times. In addition, since the prediction process is mathematically a matrix operation process, there is no convergence problem, which there is in a TCAD simulation.

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“…Recently, machine learning (ML)-based device optimization frameworks have been proposed to handle the complexity of the optimization of next-generation MOSFET devices [13]- [15]. ML can model the relationships between the design variables and device characteristics based on the data.…”

Section: Introductionmentioning

confidence: 99%

“…They optimized the feedback FETs by analyzing the gradient of the trained NN. In [14] and [15], the NN was trained using 1,510 TCAD simulations of lateral double-diffused MOS. The device was optimized by combining the NN with a Bayesian optimizer in a 4-dimensional (4D) design space.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimization of MOSFET Devices Using Effective Stopping Condition

Kim

Shin

2021

IEEE Access

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Current nanometer-scale metal-oxide-semiconductor field-effect transistor (MOSFET) devices exhibit short-channel, quantum, and self-heating effects, making modeling and analysis very complex. A few recent works have employed machine-learning (ML) techniques and neural networks (NN) to model the complex relationships and optimize devices, but a problem with the NN-based device optimization is that it is data-intensive. Bayesian optimization (BO) can realize ML-based data-efficient optimization of the MOSFET device, as it finds the global optimum while requiring few training data. BO stops theoretically when every candidate is explored, so previous works used a fixed number of iterations for the stopping condition. Such an empirical stopping condition is detrimental to the efficiency and reliability of BO, because the global optimum can be found at an earlier stage or even after stopping. Recently, maximum expected improvement (EI max ) with a tiny constant has been proposed as a stopping condition for BO. However, there have not been sufficient works for improving efficiency of BO. By advancing the EI max scheme, we have systemically investigated the effective stopping condition (ESC) for BO of MOSFET devices to boost the efficiency and reliability of optimization. We found that EI max less than a 1% of unit value was an efficient and reliable ESC for optimization, which resulted in upto-87.6% and up-to-47% reductions of required training data compared with the fixed iteration method and the tiny constant method, respectively. Our study provides a novel method to boost efficiency and reliability of BOs for the optimization of MOSFET design in the semiconductor industry.

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Powernet: SOI Lateral Power Device Breakdown Prediction With Deep Neural Networks

Cited by 43 publications

References 29 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

Prediction of Static Characteristic Parameters of an Insulated Gate Bipolar Transistor Using Artificial Neural Network

Bayesian Optimization of MOSFET Devices Using Effective Stopping Condition

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