Prediction of FinFET Current-Voltage and Capacitance-Voltage Curves Using Machine Learning With Autoencoder

Mehta, Kashyap; Wong, Helen

doi:10.1109/led.2020.3045064

Cited by 88 publications

(40 citation statements)

References 11 publications

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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: 69%

“…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].…”

Section: Introductionmentioning

confidence: 99%

“…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]. In addition, machine learning is applied to predict the current value of IGBTs in circuits and suppress the current imbalance of parallel-connected IGBTs through artificial neural networks, or to predict the remaining lifetime of the IGBTs operating in circuits [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Methodsmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…However, the machine learning (ML) techniques that have emerged in recent years provide a potential means to effectively predict these devices’ performance without using physical-based models [ 6 , 7 , 8 ]. By using an artificial neural network (ANN), the ML-based methods can explore the latent relationship between input and output data via training a neural network that is constructed by several hidden-layer.…”

Section: Introductionmentioning

confidence: 99%

Off-State Performance Characterization of an AlGaN/GaN Device via Artificial Neural Networks

et al. 2022

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Due to the complexity of the 2D coupling effects in AlGaN/GaN HEMTs, the characterization of a device’s off-state performance remains the main obstacle to exploring the device’s breakdown characteristics. To predict the off-state performance of AlGaN/GaN HEMTs with efficiency and veracity, an artificial neural network-based methodology is proposed in this paper. Given the structure parameters, the off-state current–voltage (I–V) curve can therefore be obtained along with the essential performance index, such as breakdown voltage (BV) and saturation leakage current, without any physics domain requirement. The trained neural network is verified by the good agreement between predictions and simulated data. The proposed tool can achieve a low average error of the off-state I–V curve prediction (Ave. Error < 5%) and consumes less than 0.001‰ of average computing time than in TCAD simulation. Meanwhile, the convergence issue of TCAD simulation is avoided using the proposed method.

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“…Artificial neural networks (ANNs) have widely served as compact models for semiconductor device applications [25][26][27][28][29][30]. Recently, with the surge of machine learning applications, efficient modeling methodologies for ANN training were developed and applied to modeling and simulation problems for advanced transistors [31][32][33][34]. Compared with physical models, the ANN models are fast, adaptable, accurate, and technology-independent for predicting a high nonlinearity of HF noise characteristics in quasi-ballistic MOSFETs.…”

Section: Introductionmentioning

confidence: 99%

Physics-Informed Neural Network for High Frequency Noise Performance in Quasi-Ballistic MOSFETs

Lee

2021

Electronics

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A physics-informed neural network (PINN) model is presented to predict the nonlinear characteristics of high frequency (HF) noise performance in quasi-ballistic MOSFETs. The PINN model is formulated by combining the radial basis function-artificial neural networks (RBF-ANNs) with an improved noise equivalent circuit model, including all the noise sources. The RBF-ANNs are utilized to model the thermal channel noise, induced gate noise, correlation noise, as well as the shot noise, due to the gate and source-drain tunneling current through the potential barriers. By training a spatial distribution of the thermal channel noise and a Fano factor of the shot noise, underlying physical theories are naturally embedded into the PINN model as prior information. The PINN model shows good capability of predicting the noise performance at high frequencies.

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Prediction of FinFET Current-Voltage and Capacitance-Voltage Curves Using Machine Learning With Autoencoder

Cited by 88 publications

References 11 publications

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

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

Off-State Performance Characterization of an AlGaN/GaN Device via Artificial Neural Networks

Physics-Informed Neural Network for High Frequency Noise Performance in Quasi-Ballistic MOSFETs

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