Researchers Explain 1/f Noise Extrema in Boron Nitride Heterostructures Via Charge Carrier Trapping

The ubiquitous flicker, or 1/f, noise presents a fundamental limitation in many electronic devices, and understanding its origins in emerging materials is crucial for future technologies. Kazakov and Valitov, researchers at [institution information not provided in source], now demonstrate that charge-carrier trapping by impurities significantly influences this noise in monolayer graphene. Their work explains the observed multiple peaks and troughs in the noise’s frequency dependence, revealing how the probability of trapping, and a specific energy threshold, combine to shape the noise characteristics. This detailed analysis provides a new framework for understanding noise in graphene and offers a pathway to control and minimise it, potentially improving the performance of graphene-based electronics.

Impurity Trapping Explains Boron Nitride Noise

Researchers have significantly advanced the understanding of 1/f noise, a common phenomenon in conducting materials, by demonstrating a clear connection between charge carrier trapping and its behavior in graphene-boron nitride heterostructures. The team discovered that fluctuations in voltage across these materials are not simply random, but are influenced by impurities within the boron nitride component that trap charge carriers. This work moves beyond previous models which often required adjustable parameters to fit experimental data, offering a more fundamental explanation for observed noise characteristics. The investigation involved developing a detailed kinetic equation that describes how charge carriers interact with acoustic phonons while also being subject to trapping by impurities.

By solving this equation numerically, scientists were able to predict the frequency exponent, a key measure of 1/f noise, as a function of charge carrier concentration. The model accurately reproduces the complex patterns observed in experimental data, revealing that a sufficiently wide trapping probability, coupled with an energy threshold, leads to multiple minima and maxima in the exponent’s behavior. This prediction directly addresses experimental observations of non-monotonicity in the frequency exponent as a function of charge carrier concentration and temperature in hBN heterostructures. Crucially, the research provides a quantitative link between the trapping process and the observed noise, allowing for estimations of both the energy threshold for trapping and the trapping cross-section. This breakthrough delivers a more complete and physically grounded understanding of 1/f noise in these materials, paving the way for improved design and performance of graphene-based electronic devices and sensors, and confirms that a fundamental understanding of material imperfections is critical for controlling and minimizing noise in nanoscale electronics.