Neural Network-Based and Modeling With High Accuracy and Potential Model Speed<i />
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Tung, Chien-Ting; Kao, Ming-Yen; Hu, Chenming

doi:10.1109/ted.2022.3208514

Cited by 26 publications
(13 citation statements)

References 16 publications

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“…The higher-order derivative of the GST means that the higherorder harmonic analysis is more accurate. Differentiation appeared up to the third order and the fourth order [12], [13], respectively. In this paper, the smooth function was used in the data preprocessing phase to improve the non-differentiation point as the order increased, and the accuracy of the model was verified through AC simulation of the analog circuit.…”

Section: Circuitsmentioning
confidence: 99%

Fast and Expandable ANN-Based Compact Model and Parameter Extraction for Emerging Transistors
Jeong
¹
,
Woo
²
,
Choi
³

et al. 2023
IEEE J. Electron Devices Soc.
17
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1
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Section: Circuitsmentioning
confidence: 99%

“…These new challenges increase the difficulty of modeling new emerging devices for three reasons: (a) the traditional standard FET models cannot well-capture the electrical characteristics of emerging devices, (b) developing the physics-based model equation requires a long time and expertise, and (c) for equation-based models, it is still challenging to fully automate the model parameter extraction process while achieving a very high fitting accuracy [ 5 ]. In the previous studies [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], Neural Networks (NN) show promising accuracy in emerging device modeling. However, NN-based device modeling suffers from two main issues: unphysical behaviors and needing NN expertise [ 5 ].…”

Section: Introductionmentioning
confidence: 99%

“…Huang et al [ 18 ] incorporated a physical-relation-neural-network to map between device parameters and surface potential, then constructed by mathematical equations, which may induce additional errors for emerging devices. Tung et al [ 10 ] used a loss function to smooth the output, but this approach only works on oversampled data. When the input electrical parameters are increased, the oversample may result in an unacceptable data size.…”

Section: Introductionmentioning
confidence: 99%

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A Physics-Informed Automatic Neural Network Generation Framework for Emerging Device Modeling
Guangxin
¹
,
You
²
,
Li
³

et al. 2023
Micromachines
1
0
0
0
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With the rapid development of semiconductor technology, traditional equation-based modeling faces challenges in accuracy and development time. To overcome these limitations, neural network (NN)-based modeling methods have been proposed. However, the NN-based compact model encounters two major issues. Firstly, it exhibits unphysical behaviors such as un-smoothness and non-monotonicity, which hinder its practical use. Secondly, finding an appropriate NN structure with high accuracy requires expertise and is time-consuming. In this paper, we propose an Automatic Physical-Informed Neural Network (AutoPINN) generation framework to solve these challenges. The framework consists of two parts: the Physics-Informed Neural Network (PINN) and the two-step Automatic Neural Network (AutoNN). The PINN is introduced to resolve unphysical issues by incorporating physical information. The AutoNN assists the PINN in automatically determining an optimal structure without human involvement. We evaluate the proposed AutoPINN framework on the gate-all-around transistor device. The results demonstrate that AutoPINN achieves an error of less than 0.05%. The generalization of our NN is promising, as validated by the test error and the loss landscape. The results demonstrate smoothness in high-order derivatives, and the monotonicity can be well-preserved. We believe that this work has the potential to accelerate the development and simulation process of emerging devices.
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“…[12] As an alternative to physics-based models, neural compact models that utilize neural networks (NNs) to reproduce device behaviors without requiring domain expertise have been proposed. [13] However, most research on neural compact models has focused on modeling a single device without parameters to adjust model behavior, [14] limiting the applicability of these methods to DTCO because they cannot capture the variations in different devices under different conditions. Previous studies have suggested neural compact models that can describe multiple devices using model parameters, [15] but they require a trade-off between simulation program with integrated circuit emphasis (SPICE) simulation speed and accuracy, considering that both metrics depend on network size.…”

mentioning
confidence: 99%

Neural Compact Modeling Framework for Flexible Model Parameter Selection with High Accuracy and Fast SPICE Simulation
Eom,
Yun,
Jang
et al. 2024
Advanced Intelligent Systems
2
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0
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Neural compact models are proposed to simplify device‐modeling processes without requiring domain expertise. However, the existing models have certain limitations. Specifically, some models are not parameterized, while others compromise accuracy and speed, which limits their usefulness in multi‐device applications and reduces the quality of circuit simulations. To address these drawbacks, a neural compact modeling framework with a flexible selection of technology‐based model parameters using a two‐stage neural network (NN) architecture is proposed. The proposed neural compact model comprises two NN components: one utilizes model parameters to program the other, which can then describe the current–voltage (I–V) characteristics of the device. Unlike previous neural compact models, this two‐stage network structure enables high accuracy and fast simulation program with integrated circuit emphasis (SPICE) simulation without any trade‐off. The I–V characteristics of 1000 amorphous indium–gallium–zinc‐oxide thin‐film transistor devices with different properties obtained through fully calibrated technology computer‐aided design simulations are utilized to train and test the model and a highly precise neural compact model with an average IDS error of 0.27% and R2 DC characteristic values above 0.995 is acquired. Moreover, the proposed framework outperforms the previous neural compact modeling methods in terms of SPICE simulation speed, training speed, and accuracy.

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Neural Network-Based and Modeling With High Accuracy and Potential Model Speed

Cited by 26 publications

References 16 publications

Fast and Expandable ANN-Based Compact Model and Parameter Extraction for Emerging Transistors

Fast and Expandable ANN-Based Compact Model and Parameter Extraction for Emerging Transistors

A Physics-Informed Automatic Neural Network Generation Framework for Emerging Device Modeling

Neural Compact Modeling Framework for Flexible Model Parameter Selection with High Accuracy and Fast SPICE Simulation

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