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Two-dimensional nanomaterials have obvious advantages in thermoelectric device development. It is rare to use the same experimental system to accurately measure multiple thermoelectrical parameters of the same sample. Therefore, scholars have developed suspended microdevices, T-type and H-type methods to fulfill the abovementioned requirements. These methods usually require a direct-current voltage signal to detect in Seebeck coefficient measurement. However, the thermoelectric potential generated by the finite temperature difference is very weak and can be easily overwritten by the direct-current voltage, thereby affecting the measurement accuracy. In addition, these methods generally require specific electrodes to measure the thermoelectric potential. We propose a measurement method that combines laser heating with an H-type device. By introducing a temperature difference in two-dimensional materials through laser heating, the thermoelectric potential can be accurately measured. This method does not require specific electrodes to simplify the device structure. The thermoelectrical parameters of supported graphene are successfully measured with this method; the results are in good agreement with the literature. The proposed method is unaffected by material size and characteristics. It has potential application value in the characterization of thermoelectric physical properties.
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Wide-bandgap gallium nitride (GaN)-based semiconductors offer significant advantages over traditional Si-based semiconductors in terms of high-power and high-frequency operations. As it has superior properties, such as high operating temperatures, high-frequency operation, high breakdown electric field, and enhanced radiation resistance, GaN is applied in various fields, such as power electronic devices, renewable energy systems, light-emitting diodes, and radio frequency (RF) electronic devices. For example, GaN-based high-electron-mobility transistors (HEMTs) are used widely in various applications, such as 5G cellular networks, satellite communication, and radar systems. When a current flows through the transistor channels during operation, the self-heating effect (SHE) deriving from joule heat generation causes a significant increase in the temperature. Increases in the channel temperature reduce the carrier mobility and cause a shift in the threshold voltage, resulting in significant performance degradation. Moreover, temperature increases cause substantial lifetime reductions. Accordingly, GaN-based HEMTs are operated at a low power, although they have demonstrated high RF output power potential. The SHE is expected to be even more important in future advanced technology designs, such as gate-all-around field-effect transistor (GAAFET) and three-dimensional (3D) IC architectures. Materials with high thermal conductivities, such as silicon carbide (SiC) and diamond, are good candidates as substrates for heat dissipation in GaN-based semiconductors. However, the thermal boundary resistance (TBR) of the GaN/substrate interface is a bottleneck for heat dissipation. This bottleneck should be reduced optimally to enable full employment of the high thermal conductivity of the substrates. Here, we comprehensively review the experimental and simulation studies that report TBRs in GaN-on-SiC and GaN-on-diamond devices. The effects of the growth methods, growth conditions, integration methods, and interlayer structures on the TBR are summarized. This study provides guidelines for decreasing the TBR for thermal management in the design and implementation of GaN-based semiconductor devices.
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The band gap of rutile TiO2 has been narrowed, via the formation of oxygen vacancies (OVs) during heat treatment in carbon powder (cHT) with embedding TiO2 coatings. The narrowed band gap efficiently improves the visible light response of TiO2 coatings, to further enhance the visible-light-driven photocatalytic activity. The change in OVs during cHT has been studied by manipulation of cHT temperature and time. The effect of OVs on the band structure of nonstoichiometric TiO2-x has been further calculated by first-principles calculations. With raising the temperature, SEM images show that the nano-size fiber-like structure forms on the surface of TiO2 coatings, and the amount of the fiber-like structure significantly increases and their size changes from nano to micro under 800 °C, contributing to cause an increase in accessible surface area. The UV-Vis results reveal that the band gap of TiO2 has been narrowed during cHT, due to the formed oxygen vacancies. The XPS results further confirm that the formation of surface defects including OVs, and the XPS depth profile further shows the decreased relative amount of O whereas increased relative amount of carbon. Notably, after cHT for TiO2 coatings, the photocatalytic activity first increases then decreases with raising the temperature, achieving approximately 3 times at 850 °C. The first-principles calculation suggest that the OVs in TiO2 coatings with localized electrons could facilitate the band gap narrowing, further favoring to enhance the photocatalytic activity under visible light.
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As the human excreta, urine is often used as one of the test materials in medical research due to its composition and content directly reflecting the health status of the body. Considering that the substances in urine may show different effects on its freezing process, solidification characteristics of sessile urine droplets on a horizontal cold plate surface under natural convection were experimentally investigated by comparing with those of water droplets under same conditions. To make the conclusion analysis more reasonable, the urine of a human without any diseases, especially metabolic diseases, was treated and used. The characteristics include nucleation location, dynamic variation of droplet color, and temperatures at different heights inside the droplet, and so forth. It was found that, similar to that of a water droplet, the solidification process of a urine droplet also experiences the following four stages: supercooling, recalescence, freezing, and cooling, in chronological order. Differently, the urine droplet changes from transparent to blur white at the supercooling stage due to the precipitation of inorganic salts. For nucleation locations, 46.67% cases are at the bottom, while others are at the top and middle of urine droplets. For a 10 µL droplet on a surface of -30 °C, urine has a 0.95 s freezing duration shorter than water, and a 5.31 °C lower phase-transition temperature. Results of this study are expected to reflect the content of substances in urine and thus provide references for urinalysis of patients with metabolic diseases.
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Convección , Agua , Congelación , Humanos , Transición de Fase , Temperatura de TransiciónRESUMEN
Ruthenium may replace copper interconnects in next-generation very-large-scale integration (VLSI) circuits. However, interfacial bonding between Ru interconnect wires and surrounding dielectrics must be optimized to reduce thermal boundary resistance (TBR) for thermal management. In this study, various adhesion layers are employed to modify bonding at the Ru/SiO2 interface. The TBRs of film stacks are measured using the frequency-domain thermoreflectance technique. TiN and TaN with high nitrogen contents significantly reduce the TBR of the Ru/SiO2 interface compared to common Ti and Ta adhesion layers. The adhesion layer thickness, on the other hand, has only minor effect on TBR when the thickness is within 2-10 nm. Hard X-ray photoelectron spectroscopy of deeply buried layers and interfaces quantitatively reveals that the decrease in TBR is attributed to the enhanced bonding of interfaces adjacent to the TaN adhesion layer, probably due to the electron transfer between the atoms at two sides of the interface. Simulations by a three-dimensional electrothermal finite element method demonstrate that decreasing the TBR leads to a significantly smaller temperature increase in the Ru interconnects. Our findings highlight the importance of TBR in the thermal management of VLSI circuits and pave the way for Ru interconnects to replace the current Cu-based ones.
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In microthermoelectric generators (µTEGs), parasitic thermal resistance must be suppressed to increase the temperature difference across thermocouples for optimum power generation. A thermally conductive (TC) layer is typically used in µTEGs to guide the heat flow from the heat source to the hot junction of each thermocouple. In this study, we investigate the effect of the thermal boundary resistance (TBR) in metal/dielectric TC layers on the power generation of silicon nanowire (SiNW) µTEGs. We prepared various metal/adhesion/dielectric TC layers using different metal, adhesion, and dielectric layers and measured the thermal resistance using the frequency-domain thermoreflectance method. We found that the thermal resistance was significantly different, mainly due to the TBR of the metal/dielectric interfaces. Interface characterization highlights the significant role of the interfacial bonding strength and interdiffusion in TBR. We fabricated a prototype SiNW-µTEG with different TC layers for testing, finding that the power generation increased significantly when the thermal resistance of the TC layer was lowered. This study helps to understand the underlying physics of thermal transport at interfaces and provides a guideline for the design and fabrication of µTEGs to enhance power generation for effective energy harvesting.
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Temperature increase in the continuously narrowing interconnects accelerates the performance and reliability degradation of very large scale integration (VLSI). Thermal boundary resistance (TBR) between an interconnect metal and dielectric interlayer has been neglected or treated approximately in conventional thermal analyses, resulting in significant uncertainties in performance and reliability. In this study, we investigated the effects of TBR between an interconnect metal and dielectric interlayer on temperature increase of Cu, Co, and Ru interconnects in deeply scaled VLSI. Results indicate that the measured TBR is significantly higher than the values predicted by the diffuse mismatch model and varies widely from 1 × 10-8 to 1 × 10-7 m2 K W-1 depending on the liner/barrier layer used. Finite element method simulations show that such a high TBR can cause a temperature increase of hundreds of degrees in the future VLSI interconnect. Characterization of interface properties shows the significant importance of interdiffusion and adhesion in TBR. For future advanced interconnects, Ru is better than Co for heat dissipation in terms of TBR. This study provides a guideline for the thermal management in deeply scaled VLSI.
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Heat transfer at interfaces plays a critical role in material design and device performance. Higher interfacial thermal resistances (ITRs) affect the device efficiency and increase the energy consumption. Conversely, higher ITRs can enhance the figure of merit of thermoelectric materials by achieving ultra-low thermal conductivity via nanostructuring. This study proposes a dataset of descriptors for predicting the ITRs. The dataset includes two parts: one part consists of ITRs data collected from 87 experimental papers and the other part consists of the descriptors of 289 materials, which can construct over 80,000 pair-material systems for ITRs prediction. The former part is composed of over 1300 data points of metal/nonmetal, nonmetal/nonmetal, and metal/metal interfaces. The latter part consists of physical and chemical properties that are highly correlated to the ITRs. The synthesis method of the materials and the thermal measurement technique are also recorded in the dataset for further analyses. These datasets can be applied not only to ITRs predictions but also to thermal-property predictions or heat transfer on various material systems.
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An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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For harvesting energy from waste heat, the power generation densities and fabrication costs of thermoelectric generators (TEGs) are considered more important than their conversion efficiency because waste heat energy is essentially obtained free of charge. In this study, we propose a miniaturized planar Si-nanowire micro-thermoelectric generator (SiNW-µTEG) architecture, which could be simply fabricated using the complementary metal-oxide-semiconductor-compatible process. Compared with the conventional nanowire µTEGs, this SiNW-µTEG features the use of an exuded thermal field for power generation. Thus, there is no need to etch away the substrate to form suspended SiNWs, which leads to a low fabrication cost and well-protected SiNWs. We experimentally demonstrate that the power generation density of the SiNW-µTEGs was enhanced by four orders of magnitude when the SiNWs were shortened from 280 to 8 µm. Furthermore, we reduced the parasitic thermal resistance, which becomes significant in the shortened SiNW-µTEGs, by optimizing the fabrication process of AlN films as a thermally conductive layer. As a result, the power generation density of the SiNW-µTEGs was enhanced by an order of magnitude for reactive sputtering as compared to non-reactive sputtering process. A power density of 27.9 nW/cm2 has been achieved. By measuring the thermal conductivities of the two AlN films, we found that the reduction in the parasitic thermal resistance was caused by an increase in the thermal conductivity of the AlN film and a decrease in the thermal boundary resistance.
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Thermal boundary resistance (TBR) is a key property for the thermal management of high power micro- and opto-electronic devices and for the development of high efficiency thermal barrier coatings and thermoelectric materials. Prediction of TBR is important for guiding the discovery of interfaces with very low or very high TBR. In this study, we report the prediction of TBR by the machine learning method. We trained machine learning models using the collected experimental TBR data as training data and materials properties that might affect TBR as descriptors. We found that the machine learning models have much better predictive accuracy than the commonly used acoustic mismatch model and diffuse mismatch model. Among the trained models, the Gaussian process regression and the support vector regression models have better predictive accuracy. Also, by comparing the prediction results using different descriptor sets, we found that the film thickness is an important descriptor in the prediction of TBR. These results indicate that machine learning is an accurate and cost-effective method for the prediction of TBR.
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In this research, a technique for measuring ultrasonic power with a mechanoluminescent (ML) sensing film was developed. A linear relationship was observed between the ultrasonic power and the ML intensity induced by ultrasonic vibration, indicating that ultrasonic power can be evaluated by measuring ML intensity. In addition, the ultrasonic power distribution on the surface of a transducer was visualized by recording ML images with a charge-coupled device camera.