Moving the heat aside with BAs: Thermal management becomes increasingly important as we decrease device size and increase computing power. Engineering materials with high thermal conductivity, such as boron arsenide (BAs), is hard because it is essential to avoid defects and impurities during synthesis, which would stop heat flow. Three different research groups have synthesized BAs with a thermal conductivity around 1000 watts per meter-kelvin: Kang et al. , Li et al. , and Tian et al. succeeded in synthesizing high-purity BAs with conductivities half that of diamond but more than double that of conventional metals (see the Perspective by Dames). The advance validates the search for high-thermal-conductivity materials and provides a new material that may be more easily integrated into semiconducting devices. Science , this issue p. 575 , p. 579 , p. 582 ; see also p. 549

Two different four-probe thermal and thermoelectric measurement methods have been reported for measuring the thermal conductivity, Seebeck coefficient, and electrical conductivity of suspended thin films and nanostructures with microfabricated electro-thermal transducers. The thermal contact resistance was extracted from the measured thermoelectric voltage drop at the contacts in the earlier four-probe method based on the assumption of constant thermal and thermoelectric properties along the sample. In comparison, the latter four-probe method can directly obtain the contact thermal resistance together with the intrinsic sample thermal resistance without making this assumption. Here, the measurement theory and data reduction processes of the latter four-probe measurement method are re-examined and improved. The measured thermal conductivity result of this improved method on representative thin film samples are found to agree with those obtained from the earlier four-probe method, which has obtained similar Seebeck coefficient and electrical conductivity as those measured with a different method for a supported thin film. The agreement provides further validation of the latest four-probe thermal transport measurement method of thin films and nanostructures.

We report in-plane thermoelectric measurements of Bi₂Te₃ nanoplates, a typical topological insulator with Dirac-like metallic surface states, grown by chemical vapor deposition. The as-grown flakes exposed to ambient conditions exhibit relatively small thermopowers around −34 μV/K due to unintentional surface doping (e.g., gas adsorption and surface oxidation). After removal of the unintentional surface doping and surface passivation by deposition of 30 nm of Al₂O₃ using atomic layer deposition (ALD), the Seebeck coefficient of these flakes increases by a factor of 5× to −169 μV/K. Here, we believe that the ALD-based surface passivation can prevent the degradation of the thermoelectric properties caused by gas adsorption and surface oxidation processes, thus, reducing the unintentional doping in the Bi₂Te₃ and increasing the Seebeck coefficient. The high surface-to-volume ratio of these thin (∼10 nm thick) nanoplates make them especially sensitive to surface doping, which is a common problem among nanomaterials in general. An increase in the sample resistance is also observed after the ALD process, which is consistent with the decrease in doping.

Compared to other extrinsic phonon scattering mechanisms such as surface and interior defects, phonon scattering and lattice thermal resistance due to structural rippling in few-layer two-dimensional (2D) materials are under-examined. Here, the temperature-dependent basal-plane thermal conductivities (κ) of one rippled and four flat molybdenum disulfide (MoS₂) samples are measured using a four-probe thermal transport measurement method. A flat 18 nm thick sample and a rippled 20 nm thick sample show similar peak κ values of 122 ± 17 and 129 ± 19 W m⁻¹ K⁻¹, respectively. In comparison, a 32 nm thick flat sample has a peak κ value of only 58 ± 11 W m⁻¹ K⁻¹ despite having an increased thickness. The peak thermal conductivities of the five samples decrease with increasing integrated Raman intensity caused by defects in the frequency range of the phonon bandgap in MoS₂. In conjunction with the experimental findings, theoretical calculations of the temperature-, thickness-, strain-, and defect-dependent κ of thin MoS₂ layers reveal the importance of interior defect scattering over scattering from compression-induced ripples and surface defects in these samples. The results further clarify the conditions where ripples are important in determining the basal plane thermal resistance in layered systems.

A high-speed atomic force microscope (HS-AFM) requires a specialized set of hardware and software and therefore improving video-rate HS-AFMs for general applications is an ongoing process. To improve the imaging rate of an AFM, all components have to be carefully redesigned since the slowest component determines the overall bandwidth of the instrument. In this work, we present a design of a compact HS-AFM scan-head featuring minimal loading on the Z-scanner. Using a custom-programmed controller and a high-speed lateral scanner, we demonstrate its working by obtaining topographic images of Blu-ray disk data tracks in contact- and tapping-modes. Images acquired using a contact-mode cantilever with a natural frequency of 60 kHz in constant deflection mode show good tracking of topography at 400 Hz. In constant height mode, tracking of topography is demonstrated at rates up to 1.9 kHz for the scan size of $1\mathsf{\mu }\mathrm{m}\times 1\mathsf{\mu }\mathrm{m}$ with $100\times 100$ pixels.

Accurate prediction and understanding of plasma etch depth are crucial for process stability and yield in semiconductor manufacturing. While conventional machine learning approaches demonstrate the feasibility of noncontact prediction, they often lack physical interpretability and struggle with complex feature dependencies. In this study, we develop an explainable machine learning framework that integrates plasma process parameters and optical features from digital image colorimetry (DIC) to predict the remaining SiO2 thickness after plasma etching. To account for limited experimental data and measurement variability, a probabilistic treatment was applied by introducing spatial/parametric variations and input noise. Multiple regression models were systematically compared; the ensemble-based Random Forest Model (RFM) demonstrated superior performance (RMSE < 1.4 nm) and greater robustness against multicollinearity than linear approaches. To interpret model behavior, SHapley Additive exPlanations (SHAP) analysis was applied, bridging the gap between data-driven predictions and plasma physics. Our analysis reveals that radio frequency power is the most influential parameter, while the low importance of gas flow rate indicates a surface-reaction-rate limited regime. Furthermore, DIC-derived features act as a high-resolution physical proxy for thin-film interference, providing complementary information to process parameters. Beyond accuracy, this framework establishes a physically meaningful correspondence between model-derived importance and underlying etching mechanisms. This approach provides a foundation for noninvasive, interpretable, and real-time process monitoring, enhancing transparency and control in semiconductor fabrication. To facilitate reproducibility, the experimental dataset is made publicly available.