Heat dissipation and thermal management is a rising concern for nanoelectronic devices and threatens to curtail their adoption in integrated circuits, sensors, and energy converters. Joule heating due to dissipation in the channel region of nanoelectronic devices causes increased temperature and may lead to mobility degradation and long-term reliability issues. Here we study thermal transport and cross-plane thermal boundary conductance in a variety of “beyond graphene” 2D materials and few-layer stacks on several amorphous and crystalline substrates using a combination of first principles methods and Boltzmann transport of phonons. We employ machine learning to accelerate the discovery of 2D-substrate pairings with enhanced thermal conductance. Beyond that, we couple electronic and thermal transport to study dissipation in field effect MOS transistors and show that heat dissipation is non-uniform and that self-heating reduces mobility. We find that judicious selection of the number of layers and substrate can significantly reduce the deleterious effects of Joule heating.

Raman thermometry has gained immense popularity for probing the thermal properties of nanostructured materials due to its excellent spatial resolution and lack of contact error; however, it has a key weakness in its temperature resolution. In this work, we aim to improve the temperature resolution of Raman thermometry through training neural networks to track the locations, widths, and relative heights of multiple peaks at once. We find that in training a multilayer perceptron on 13 pixel values representing the Raman peak of silicon, the variance and standard deviation in thermal conductivity predictions can be reduced as compared to those resulting from the predominant method of tracking the peak location as it shifts with temperature. We expect that this work may contribute to greater accuracy of thermal measurements from non-contact Raman-based techniques and thereby improve the consensus on the thermal properties of 2D materials.

Conjugated polymers (CP) are frequently doped to modulate their transport and optical properties. Doping alters the intrinsic Gaussian density of states (DOS) by adding Coulomb energy and inducing an exponential tail. Changes in transport or optical properties are mainly tracked back to changes in DOS and carrier hopping rates. Conductivity shows a power-law like increase and the Seebeck coefficient a decrease with carrier concentration. This results in a trade-off between transport properties with doping. However, their modification with doping is still not well understood. Here we show that capture transport and optical properties of doped CPs, by developing a tight-binding Hamiltonian that includes dopant-induced energetic disorder (DID) via Coulomb interactions. We utilize perturbation theory to calculate transition rates between wavefunctions from the calculated eigenenergies and eigenfunctions. With the obtained transition rates, we solve Pauli master equation for occupational probabilities to compute transport properties of doped CPs. Additionally, we capture optical absorption features by simply simulating the joint DOS and IR absorption features via simulated AC conductivity. We anticipate our work to significantly contribute to understanding of underlying transport and optical physics of doped CPs.

Despite their potential for miniaturization, electronic devices made of 2D materials face thermal management challenges due to their reduced dimensionality, which can limit their efficiency and lifespan. Low thermal boundary conductance (TBC) is one major limiting factor in realizing efficient heat transfer to the substrate. Due to the roughness at the interface, the adhesion of 2D materials to their substrates tend to be weak, resulting in low TBC. Therefore, to improve heat flow from the 2D material, we need to discover novel ways of increasing TBC. In this study, we have used a numerical model combined with first-principles DFPT simulations to investigate a possible method to increase TBC using an electrostatic field due to gate voltage. Our study shows that electrostatic pressure can be used to effectively enhance TBC for an interface formed by a 2D material and a rough substrate. We find that electrostatic pressure can improve TBC by more than 300 % when an electric field of 3 V/nm is applied. This is due to an improvement in the vdW spring coupling constant, which shows a more than two-fold increase when a substrate roughness of 1.6 nm and correlation length of 10.8 nm, 2D-material's bending stiffness of 1.5 eV, and adhesion energy of 0.1 $J/m^{2}$ were used. We show that TBC is enhanced more when the substrate has a large roughness and small correlation length, and the $2D$ material has a large bending stiffness. This is because a stiff 2D sheet resist bending when voltage/pressure is applied, thus causing it to press more on the roughness peaks, resulting in a tremendous increase in the coupling constants at the peaks in the atomically rough surface of the substrate. However, a flexible 2D material can easily bend to conform to the topography of the rough substrate when voltage/pressure is applied, which makes the coupling constants across the interface more uniform. Here we show that TBC is enhanced more when adhesion is weak because a weak vdW bond is easily compressed by external pressure. Therefore, our study provides valuable information that can be applied in designing electronic devices with efficient heat management by using gate voltage, substrate roughness combined with the mechanical properties.

[This corrects the article DOI: 10.1021/acsomega.3c08932.].

This Letter reports the device and material investigations of enhancement-mode p-GaN-gate AlGaN/GaN high electron mobility transistors (HEMTs) for Venus exploration and other harsh environment applications. The GaN transistor in this work was subjected to prolonged exposure (11 days) in a simulated Venus environment (460 °C, 94 bar, complete chemical environment including CO2/N2/SO2). The mechanisms affecting the transistor performance and structural integrity in harsh environment were analyzed using a variety of experimental, simulation, and modeling techniques, including in situ electrical measurement (e.g., burn-in) and advanced microscopy (e.g., structural deformation). Through transistor, Transmission Line Method (TLM), and Hall-effect measurements vs temperature, it is revealed that the mobility decrease is the primary cause of reduction of on-state performance of this GaN transistor at high temperature. Material analysis of the device under test (DUT) confirmed the absence of foreign elements from the Venus atmosphere. No inter-diffusion of the elements (including the gate metal) was observed. The insights of this work are broadly applicable to the future design, fabrication, and deployment of robust III-N devices for harsh environment operation.

Mass spectrometry is a crucial technology in numerous applications, but it places stringent requirements on the detector to achieve high resolution across a broad spectrum of ion masses. Low-dimensional nanostructures offer opportunities to tailor properties and achieve performance not reachable in bulk materials. Here, an array of sharp zinc oxide wires was directly grown on a 30 nm thin, free-standing silicon nitride nanomembrane to enhance its field emission (FE). The nanomembrane was subsequently used as a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry detector. When ionized biomolecules impinge on the backside of the surface-modified nanomembrane, the current—emitted from the wires on the membrane’s front side—is amplified by the supplied thermal energy, which allows for the detection of the ions. An extensive simulation framework was developed based on a combination of lateral heat diffusion in the nanomembrane, heat diffusion along the wires, and FE, including Schottky barrier lowering, to investigate the impact of wire length and diameter on the FE. Our theoretical model suggests a significant improvement in the overall FE response of the nanomembrane by growing wires on top. Specifically, long thin wires are ideal to enhance the magnitude of the FE signal and to shorten its duration for the fastest response simultaneously, which could facilitate the future application of detectors in mass spectrometry with properties improved by low-dimensional nanostructures.

Transport properties of doped conjugated polymers (CPs) have been widely analyzed with the Gaussian disorder model (GDM) in conjunction with hopping transport between localized states. These models reveal that even in highly doped CPs, a majority of carriers are still localized because dielectric permittivity of CPs is well below that of inorganic materials, making Coulomb interactions between carriers and dopant counterions much more pronounced. However, previous studies within the GDM did not consider the role of screening the dielectric interactions by carriers. Here we implement carrier screening in the Debye-Hückel formalism in our calculations of dopant-induced energetic disorder, which modifies the Gaussian density of states (DOS). Then we solve the Pauli master equation using Miller-Abrahams hopping rates with states from the resulting screened DOS to obtain conductivity and Seebeck coefficient across a broad range of carrier concentrations and compare them to measurements. Our results show that screening has significant impact on the shape of the DOS and consequently on carrier transport, particularly at high doping. We prove that the slope of Seebeck coefficient versus electric conductivity, which was previously thought to be universal, is impacted by screening and decreases for systems with small dopant-carrier separation, explaining our measurements. We also show that thermoelectric power factor is underestimated by a factor of ∼10 at higher doping concentrations if screening is neglected. We conclude that carrier screening plays a crucial role in curtailing dopant-induced energetic disorder, particularly at high carrier concentrations.

2D materials have attracted broad attention from researchers for their unique electronic properties, which may be been further enhanced by combining 2D layers into vertically stacked van der Waals heterostructures (vdWHs). Among the superlative properties of 2D systems, thermoelectric (TE) energy conversion promises to enable targeted energy conversion, localized thermal management, and thermal sensing. However, TE conversion efficiency remains limited by the inherent tradeoff between conductivity and thermopower. In this paper, we use first-principles calculation to study graphene-based vdWHs composed of graphene layers and hexagonal boron nitride (h-BN). We compute the electronic band structures of heterostructured systems using Quantum Espresso and their TE properties using BoltzTrap2. Our results have shown that stacking layers of these 2D materials opens a bandgap, increasing it with the number of h-BN interlayers, which significantly improves the power factor (PF). We predict a PF of ∼1.0 × 1011 W K−2 m s for the vdWHs, nearly double compared to 5 × 1010 W K−2 m s that we obtained for single-layer graphene. This study gives important information on the effect of stacking layers of 2D materials and points toward new avenues to optimize the TE properties of vdWHs.

Two-dimensional van der Waals (vdW) materials exhibit a broad palette of unique and superlative properties, including high electrical and thermal conductivities, paired with the ability to exfoliate or grow and transfer single layers onto a variety of substrates thanks to the relatively weak vdW interlayer bonding. However, the same vdW bonds also lead to relatively low thermal boundary conductance (TBC) between the 2D layer and its 3D substrate, which is the main pathway for heat removal and thermal management in devices, leading to a potential thermal bottleneck and dissipation-driven performance degradation. Here, we use first-principles phonon dispersion with our 2D–3D Boltzmann phonon transport model to compute the TBC of 156 unique 2D/3D interface pairs, many of which are not available in the literature. We then employ machine learning to develop streamlined predictive models, of which a neural network and a Gaussian process display the highest predictive accuracy (RMSE [Formula: see text] 5 MW m−2 K−1 and [Formula: see text] 0.99) on the complete descriptor set. Then we perform sensitivity analysis to identify the most impactful descriptors, consisting of the vdW spring coupling constant, 2D thermal conductivity, ZA phonon bandwidth, the ZA phonon resonance gap, and the frequency of the first van Hove singularity or Boson peak. On that reduced set, we find that a decision-tree algorithm can make accurate predictions (RMSE [Formula: see text] 20 MW m−2 K−1 and [Formula: see text] 0.9) on materials it has not been trained on by performing a transferability analysis. Our model allows optimal selection of 2D-substrate pairings to maximize heat transfer and will improve thermal management in future 2D nanoelectronics.