Modeling emerging technologies using machine learning

Klemme, Florian; Prinz, Jannik; Santen, Victor M. van; Henkel, Jörg; Amrouch, Hussam

doi:10.1145/3400302.3415770

Cited by 18 publications

(15 citation statements)

References 17 publications

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“…Recent models based on the gate and drain voltages have been presented for predicting the current value [26][27][28][29]. These models are proposed to replace compact models in circuit simulation.…”

Section: (B))mentioning

confidence: 99%

Deep Neural Networks for Determining Subgap States of Oxide Thin-Film Transistors

et al. 2023

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In this study, we propose a deep neural network (DNN) model that extracts the subgap states in the channel layer of oxide thin-film transistors. We have developed a framework that includes creating a model training set, preprocessing the data, optimizing the model structure, decoding from density-of-state (DOS) parameters to current-voltage (I-V) characteristics, and evaluating the model performance and accuracy of curve fitting. We investigate in detail the effect of data preprocessing methods and model structure on the performance of the model. The primary finding is that the input data type and the last hidden layer significantly affects the performance of the regression model. Using double-type input data composed of several voltages and linear current values is more advantageous than using log-scale current. Moreover, the number of nodes in the last hidden layer of a regression model with multiple output nodes should be large enough to avoid interference between the output values. The proposed model outputs five DOS parameters, and the resulting parameters are decoded to an I-V curve through interpolation based on the nearest 32 data from the given dataset. We evaluate the model performance using the threshold voltage and on-current difference between a target curve and the decoded curve. The proposed model calibrates 97.1% of the 14,400 curves within the threshold voltage difference of 0.2V and on-current error of 5%. Hence, the proposed model is verified to effectively extract DOS parameters with high accuracy based on the current characteristics of oxide thin-film transistors. We expect to improve the efficiency of defect analysis by replacing the iterative manual technology computer aided design (TCAD) curve fitting with an automatic DNN model.

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Section: (B))mentioning

confidence: 99%

Deep Neural Networks for Determining Subgap States of Oxide Thin-Film Transistors

et al. 2023

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“…When it comes to emerging technologies, in which physicsbased models are not fully developed or even available, ML can play a major role to replace traditional modeling and provide accurate estimations based on "learning from available measurement data." Recently, Klemme et al [111] employed ML to model the negative capacitance field-effective transistor (NCFET), demonstrating the ability to predict with a high accuracy (>90%) the behavior of steep-slope transistors. They show that ML can be even employed to replace the standard cell library characterization.…”

Section: G Device and Technology Developmentmentioning

confidence: 99%

MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

Rapp

Amrouch

Lin

et al. 2022

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

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Due to the increasing size of integrated circuits (ICs), their design and optimization phases (i.e., computer-aided design, CAD) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some tradeoff between design quality and computational overhead. Machine learning (ML) can build powerful models that have successfully been employed in related domains. In this survey, we categorize how ML may be used and is used for design-time and run-time optimization and exploration strategies of ICs. A metastudy of published techniques unveils areas in computer-aided design (CAD) that are well explored and underexplored with ML, as well as trends in the employed ML algorithms. We present a comprehensive categorization and summary of the state of the art on ML for CAD. Finally, we summarize the remaining challenges and promising open research directions.

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“…The artificial neural network (ANN) is applied to model the device, with a preprocessing of the current and voltage to train the ANN model accurately [10]. A device model based on the NN acts as an intermediate between early device measurements and a later occurring compact model [11]. A fitness function based on key electrical characteristics (the off current and the subthreshold slope) is proposed to compensate for the R2 functions acting solely as the fitness function.…”

Section: Introductionmentioning

confidence: 99%

Research on Device Modeling Technique Based on MLP Neural Network for Model Parameter Extraction

Kang

Chen

et al. 2022

Applied Sciences

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The parameter extraction of device models is critically important for circuit simulation. The device models in the existing parameter extraction software are physics-based analytical models, or embedded Simulation program with integrated circuit emphasis (SPICE) functions. The programming implementation of physics-based analytical models is tedious and error prone, while it is time consuming to run the device model evaluation for the device model parameter extraction software by calling the SPICE. We propose a novel modeling technique based on a neural network (NN) for the optimal extraction of device model parameters in this paper, and further integrate the NN model into device model parameter extraction software. The technique does not require developers to understand the device model, which enables faster and less error-prone parameter extraction software developing. Furthermore, the NN model improves the extraction speed compared with the embedded SPICE, which expedites the process of parameter extraction. The technique has been verified on the BSIM-SOI model with a multilayer perceptron (MLP) neural network. The training error of the NN model is 4.14%, and the testing error is 5.38%. Experimental results show that the trained NN model obtains an extraction error of less than 6%, and its extraction speed is thousands of times faster than SPICE in device model parameter extraction.

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Modeling emerging technologies using machine learning

Cited by 18 publications

References 17 publications

Deep Neural Networks for Determining Subgap States of Oxide Thin-Film Transistors

Deep Neural Networks for Determining Subgap States of Oxide Thin-Film Transistors

MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

Research on Device Modeling Technique Based on MLP Neural Network for Model Parameter Extraction

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