Modeling of MOSFET Transistor by MLP Neural Networks

Lamamra, Kheireddine; Berrah, Smail

doi:10.1007/978-3-319-48929-2_32

Cited by 6 publications

(3 citation statements)

References 16 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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“…An interesting approach was made by Lamamra and Berrah [108] who used a genetic algorithm in addition to the NN. This enhancement alters the training phase so that not only the internal parameters of the NN (i.e., the weights) are changed but additionally the structure of the NN (topology, number of nodes, etc.).…”

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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“…A combination of NN and genetic algorithms is used in [10]. In addition to the regular training, the genetic algorithm is used to find the best structure of the NN.…”

Section: B Ml-based Transistor Models Without Prior Knowledgementioning

confidence: 99%