Knowledge-based neural network SPICE modeling for MOSFETs and its application on 2D material field-effect transistors

Qi, Guodong; Chen, Xinyu; Hu, Guangxi; Zhou, Peng; Bao, Wenzhong; Lü, Ye

doi:10.1007/s11432-021-3483-6

Cited by 5 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…proposed a knowledge‐based neural network (KNN) SPICE modeling method that applies to 2D MOSFETs and is further validated with standard data from BSIM 6, showing more than 20% accuracy improvement as compared with the traditional neural network. [ 204 ] Furthermore, the KNN‐based model was implemented in Verilog‐A to simulate MoS 2 transistors and circuits, such as ring oscillators, standard cells, and logic functional circuits, with results that matched well with the experimental measurements(Figure 13c). All this works pave the way for future circuit designs and simulations of 2DM ICs.…”

Section: Potential Applications Of 2dm Ics and Challengesmentioning

confidence: 65%

Section: Potential Applications Of 2dm Ics and Challengesmentioning

confidence: 99%

“…c) Optical photograph, schematic, simulation, and experimental results of MoS 2 inverter and 5-stage ring oscillator. Reproduced with permission [204]. Copyright 2023, Springer Nature Publishing d) Detailed block diagram of the MSCO platform.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Two‐Dimensional Semiconductors: From Device Processing to Circuit Integration

Sheng,

Dong,

Zhu

et al. 2023

Adv Funct Materials

Self Cite

View full text Add to dashboard Cite

The atomically thin nature and exceptional electrical properties of 2D materials (2DMs) have garnered significant interest in circuit applications. Researchers have developed circuits based on wafer‐level 2DM fabrication and monolithic integration in the laboratory. Numerous studies have been conducted on discrete device processes; however, circuit manufacturing is a multifaceted and methodical engineering process that demands seamless integration of multiple procedures. Notably, the optimization of crucial processes holds paramount significance in achieving expected results. This review presents the existing research on process integration of 2DM devices and circuit applications. The selection of suitable 2DMs for circuit applications is outlined, considering their excellent theoretical properties and feasible high‐quality growth processes. Drawing on highly mature semiconductor manufacturing process, while incorporating customized key processes, 2DM devices have the potential to strongly compete with, and even outperform, the conventional devices. Finally, the recent circuit applications of 2DMs are also discussed in detail. 2DM integrated circuits (2DM ICs) are now being practically applied in advanced manufacturing, transitioning from laboratory development to fabrication plant deployment. The implementation of underlying 2DM IC fabrication provides effective and unique solutions for More Moore, More than Moore, and Beyond CMOS technology routes.

show abstract

Section: Potential Applications Of 2dm Ics and Challengesmentioning

confidence: 65%

Section: Potential Applications Of 2dm Ics and Challengesmentioning

confidence: 99%

See 1 more Smart Citation

Two‐Dimensional Semiconductors: From Device Processing to Circuit Integration

Sheng,

Dong,

Zhu

et al. 2023

Adv Funct Materials

Self Cite

View full text Add to dashboard Cite

show abstract

“…Further, we investigated the practicality and performance of combining terrain indices with developed music indices on landform recognition. Ultimately, we utilized the importance assessment method of RF (Qi et al, 2023) to evaluate the contribution of different indices on landform recognition.…”

Section: Comparing With Conventional Indices In Landform Recognitionmentioning

confidence: 99%

Using music and music‐based analysis to model and to classify terrain in geomorphology

Lin,

Yu,

Chen

et al. 2024

Earth Surf Processes Landf

View full text Add to dashboard Cite

Music has long served as a bridge between science and nature, allowing for the artistic expression of natural and human‐made phenomena. While music has been used in geomorphology as an engaging teaching strategy, its application in specific scientific inquiries within geomorphology remains relatively unexplored. Drawing on the morphological similarities between music and terrain relief, this work introduced a novel music‐based method for modelling and expressing terrain relief based on the drainage basin profile (DBP). It converts terrain relief into pitches and time values according to mapping rules and then describes terrain relief in an audible form. Based on 5 sample areas and 360 drainage basins on the Loess Plateau, we developed the application of the proposed method on four core geomorphic tasks, including landform interpretation, analysis, recognition and classification. Experimental results show that (1) terrain music can interpret the terrain relief and landform evolution processes through its musical structure and rhythmic variations; (2) music derivatives are related to different terrain features, such as terrain relief, terrain variation intensity, landform evolution degree and terrain complexity, and have a well‐functioning relationship with a series of conventional terrain derivatives; (3) leveraging machine learning techniques, the terrain music method is effective for landform recognition, achieving an overall accuracy of 88.85% and a mean accuracy of 88.85%; and (4) via a case study in Northern Shaanxi, music modelling successfully divided it into 12 distinct landform regions and 8 landform types. Different landform regions exhibit clear regional boundaries and gradual transition zones, while specific landform regions share prominent terrain, spatial clustering and landform processes. Our delineation provides reasonable and effective landform differentiation but captures additional bed‐rock mountain features compared with a traditional method. This study highlights that music is not only an artistic expression but also a valuable research paradigm for a wide range of geomorphic tasks, offering fresh perspectives and enhancing our understanding of loess landforms. The results show a promising effort to integrate music theory into practical geomorphic tasks, demonstrating the potential of using music as a medium for conveying and analysing spatial information in geomorphology.

show abstract

“…Thereby, an in-depth study of ANN-based compact modeling methodology is necessary for the development and application of GAA devices and even complementary FET (CFET) devices, which are more sophisticated architectures with n-FET folded onto p-FET, in advanced technologies [27,28]. Actually, there are some interesting and meaningful studies on ANN-based device modeling that have been published in recent years [29][30][31][32]. However, most of the literature in this field have only superficially studied ANN modeling, focusing instead on its implementation in subsequent circuits or on the unique electrical properties under investigation, and lacking an in-depth understanding and full exploration of the ANNs used for modeling.…”

Section: Introductionmentioning

confidence: 99%

Compact Modeling of Advanced Gate-All-Around Nanosheet FETs Using Artificial Neural Network

Zhao,

Xu,

Tang

et al. 2024

Micromachines

View full text Add to dashboard Cite

As the architecture of logic devices is evolving towards gate-all-around (GAA) structure, research efforts on advanced transistors are increasingly desired. In order to rapidly perform accurate compact modeling for these ultra-scaled transistors with the capability to cover dimensional variations, neural networks are considered. In this paper, a compact model generation methodology based on artificial neural network (ANN) is developed for GAA nanosheet FETs (NSFETs) at advanced technology nodes. The DC and AC characteristics of GAA NSFETs with various physical gate lengths (Lg), nanosheet widths (Wsh) and thicknesses (Tsh), as well as different gate voltages (Vgs) and drain voltages (Vds) are obtained through TCAD simulations. Subsequently, a high-precision ANN model architecture is evaluated. A systematical study on the impacts of ANN size, activation function, learning rate, and epoch (the times of complete pass through the entire training dataset) on the accuracy of ANN models is conducted, and a shallow neural network configuration for generating optimal ANN models is proposed. The results clearly show that the optimized ANN model can reproduce the DC and AC characteristics of NSFETs very accurately with a fitting error (MSE) of 0.01.

show abstract

Knowledge-based neural network SPICE modeling for MOSFETs and its application on 2D material field-effect transistors

Cited by 5 publications

References 27 publications

Two‐Dimensional Semiconductors: From Device Processing to Circuit Integration

Two‐Dimensional Semiconductors: From Device Processing to Circuit Integration

Using music and music‐based analysis to model and to classify terrain in geomorphology

Compact Modeling of Advanced Gate-All-Around Nanosheet FETs Using Artificial Neural Network

Contact Info

Product

Resources

About