Graphene FET Low-Frequency Noise Model Captures Correlated Mobility Fluctuations and Validates Experimental Data

Low-frequency noise significantly limits the performance of graphene field-effect transistors, hindering their potential in advanced electronic devices, and researchers are now addressing this critical challenge with a new compact model. Nikolaos Mavredakis, Anibal Pacheco-Sanchez, and David Jiménez, from the Universitat Autònoma de Barcelona and the Universidad de Granada, developed a physics-based model that accurately captures the effects of correlated mobility fluctuations on noise within these transistors. Their work introduces a method for extracting key parameters related to charge trapping and scattering, adapted from silicon technology but refined for the unique characteristics of graphene. The resulting model demonstrates improved accuracy when validated against experimental data from a range of graphene transistors operating under diverse conditions, paving the way for more reliable and efficient graphene-based electronics.

Graphene Transistor Noise, Mechanisms and Modelling

This extensive research details investigations into low-frequency noise (LFN) and variability in Graphene Field-Effect Transistors (GFETs), aiming to create a physics-based compact model for accurate simulation and design of GFET-based circuits. Understanding the underlying noise mechanisms is crucial, as LFN and variability degrade signal quality and reliability in analog and radio frequency circuits. Existing models often lack a strong physical foundation, limiting their predictive power and scalability. The team investigated carrier number and mobility fluctuations, finding that these mechanisms are not independent; their correlation significantly impacts overall noise behavior.

Researchers developed a physics-based model rooted in GFET operation, performing detailed analysis of noise sources and developing methods to extract model parameters from experimental data. This model shows improved accuracy and predictive power compared to existing approaches, and its foundation allows for better scalability to different GFET geometries and operating conditions. Accurately modeling the correlation between carrier number and mobility fluctuations is essential for capturing the true noise behavior of GFETs. This work represents a significant advancement in understanding and modeling noise in GFETs, paving the way for the design of more reliable and high-performance GFET-based circuits.

Graphene Transistor Noise Model Parameter Extraction

Scientists developed a physics-based compact model to accurately simulate low-frequency noise (LFN) in single-layer graphene field-effect transistors (GFETs), a critical step towards commercial applications in areas like biosensors and terahertz detection. Researchers adapted methodologies from conventional silicon technologies for use with GFETs, while accounting for the unique physical characteristics of graphene, extracting parameters related to trapped charge density and Coulomb scattering. Following parameter extraction, the team investigated Hooge mobility and series resistance fluctuations, refining the LFN model to encompass a wider range of noise sources. The updated model was rigorously validated using experimental data from both long and short-channel GFETs, tested across an extended range of bias conditions, confirming its ability to accurately predict LFN behavior in diverse GFET configurations. This approach enables fast and accurate simulations of GFET circuits sensitive to LFN, essential for designing reliable and high-performance analog and radio frequency circuits, as well as sensitive sensors and detectors.

Correlated Mobility Fluctuations Model Graphene Transistors

Scientists have developed an extended compact model for single-layer graphene field-effect transistors (GFETs) that explicitly incorporates the effect of correlated mobility fluctuations on low-frequency noise (LFN). This work addresses the need to distinguish between carrier number and mobility fluctuations, previously obscured by empirical parameter adjustments. The team successfully integrated the Coulomb scattering coefficient into the LFN model, representing a significant advancement over previous approaches. Experiments reveal that the inclusion of this coefficient yields more physically correct parameter values, ensuring the model’s validity and scalability for industrial testing.

Researchers demonstrated that the model accurately captures the dominant physical mechanisms contributing to LFN, specifically carrier number fluctuations due to trapping and detrapping, and correlated mobility fluctuations. Measurements confirm that the proposed model assumes a uniform spatial distribution of traps near the dielectric interface, consistent with observations of the typical 1/f noise trend in GFETs. The team’s model builds upon a previously validated GFET model, extending its capabilities to include LFN analysis from DC up to radio frequency regimes. This comprehensive approach ensures consistent integration of the LFN module within the complete modeling framework, a feature not currently available in other published models. Data shows that the model accurately predicts device behavior, providing a crucial tool for optimizing GFET performance in applications such as biosensors and terahertz detection. The researchers established that variations in current and 1/f noise are identical mechanisms for GFETs, organic FETs, and CMOS technologies, further validating the model’s broad applicability.

Graphene Noise Model Validated by Experiment

This work presents a refined physics-based model for low-frequency noise in graphene field-effect transistors, incorporating the effects of correlated mobility fluctuations through Coulomb scattering. The researchers developed a parameter extraction methodology, adapted from established silicon technology processes, specifically tailored to the unique characteristics of graphene devices. This approach allows for the accurate determination of key parameters governing noise behavior in both the p-type and n-type regions of the transistor, ensuring the model’s validity, accuracy, and scalability. The team successfully validated the improved model against experimental data from a variety of graphene transistors, encompassing both short and long channel devices, and operating across a wide range of bias conditions. By sequentially extracting parameters under physical constraints, they minimized correlations and enhanced the robustness of the methodology, yielding physically meaningful values.