Deep Learning-Based BSIM-CMG Parameter Extraction for 10-nm FinFET

Kao, Ming-Yen; Chavez, Fredo; Khandelwal, Sourabh; Hu, Chenming

doi:10.1109/ted.2022.3181536

Cited by 27 publications

(2 citation statements)

References 8 publications

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“…To address these challenges, researchers have delved into various optimization-based techniques for parameter extraction, including the application of genetic algorithms (GA) [1][2][3] and the implementation of deep learning methodologies (DL) [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Current-Voltage Modeling of DRAM Cell Transistor Using Genetic Algorithm and Deep Learning

Park,

Kim,

Lee

et al. 2024

IEEE Access

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Accurate current-voltage (I-V) modeling based on the Berkeley short-channel insulated-gate field-effect transistor model (BSIM) is pivotal for integrated circuit simulation. However, the current BSIM model does not support a buried-channel-array transistor (BCAT), which is the structure of the state-of-theart commercial dynamic random access memory (DRAM) cell transistor. In this work, we propose an intelligent I-V modeling technique that combines genetic algorithm (GA) and deep learning (DL). This hybrid technique facilitates both optimization of BSIM parameter and accurate I-V modeling, even for devices not originally supported by BSIM. Additionally, we extended application of the DL to model one of the principal degradation mechanisms of transistor, the hot-carrier degradation (HCD). The successful modeling results of I-V characteristic and device degradation demonstrated that devices not supported by BSIM can be accurately modeled for integrated circuit simulations.

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Section: Introductionmentioning

confidence: 99%

Current-Voltage Modeling of DRAM Cell Transistor Using Genetic Algorithm and Deep Learning

Park,

Kim,

Lee

et al. 2024

IEEE Access

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“…25 Additionally, deep NN have been employed for generating black-box SPICE models 26,27 and extracting parameters for the industrial standard BSIM model. 28,29 To bring a new semiconductor technology into mass production, it is crucial to take into account various factors, such as materials, device design, and system integration. These factors result in tremendous number of design variables that must be carefully considered.…”

mentioning

confidence: 99%

Physics-Integrated Machine Learning for Efficient Design and Optimization of a Nanoscale Carbon Nanotube Field-Effect Transistor

Fan,

Low

2023

ECS J. Solid State Sci. Technol.

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We propose a framework for optimizing the design of a carbon nanotube field-effect transistor (CNTFET) through the integration of device physics, machine learning (ML), and multi-objective optimization (MOO). First, we leverage the calibrated TCAD model based on experimental data to dissect the physical mechanisms of the CNTFET, gaining insights into its operational principles and unique physical properties. This model also serves as a foundation, enabling multi-scale performance evaluations essential for dataset construction. In the ML phase, a novel chain structure of support vector regression guided by a comprehensive statistical analysis of the design metrics yields a notable 97.35% accuracy without overfitting, even with limited data. The established ML model exhibits its competence in rapidly producing a global response surface for multi-scale CNTFET. Remarkably, an unusual trend within the CNTFET's behavior has been identified through this process, for which a physics explanation has been provided. We further compare shallow and deep learning-based TCAD digital twins for model selection guidance. Using the non-dominated sorted genetic algorithm-II (NSGA-II) in MOO, we harmonize metrics at both device and circuit levels, significantly reducing the design space. The closed-loop framework expedites the early-stage development of advanced transistors, overcoming the challenges posed by limited data.

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Data-driven multi-objective optimization with neural network-based sensitivity analysis for semiconductor devices

Lee

Kim

et al. 2023

Engineering Applications of Artificial Intelligence

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Deep Learning-Based BSIM-CMG Parameter Extraction for 10-nm FinFET

Cited by 27 publications

References 8 publications

Current-Voltage Modeling of DRAM Cell Transistor Using Genetic Algorithm and Deep Learning

Current-Voltage Modeling of DRAM Cell Transistor Using Genetic Algorithm and Deep Learning

Physics-Integrated Machine Learning for Efficient Design and Optimization of a Nanoscale Carbon Nanotube Field-Effect Transistor

Data-driven multi-objective optimization with neural network-based sensitivity analysis for semiconductor devices

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