β-phase gallium oxide ( β-Ga2O3) has drawn significant attention due to its large critical electric field strength and the availability of low-cost high-quality melt-grown substrates. Both aspects are advantages over gallium nitride (GaN) and silicon carbide (SiC) based power switching devices. However, because of the poor thermal conductivity of β-Ga2O3, device-level thermal management is critical to avoid performance degradation and component failure due to overheating. In addition, for high-frequency operation, the low thermal diffusivity of β-Ga2O3 results in a long thermal time constant, which hinders the use of previously developed thermal solutions for devices based on relatively high thermal conductivity materials (e.g., GaN transistors). This work investigates a double-side diamond-cooled β-Ga2O3 device architecture and provides guidelines to maximize the device’s thermal performance under both direct current (dc) and high-frequency switching operation. Under high-frequency operation, the use of a β-Ga2O3 composite substrate (bottom-side cooling) must be augmented by a diamond passivation overlayer (top-side cooling) because of the low thermal diffusivity of β-Ga2O3.
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A review of the growth, doping, and applications of Beta-Ga2O3 thin films
β-Ga2O3 is emerging as an interesting wide band gap semiconductor for solar blind photo detectors (SBPD) and high power field effect transistors (FET) because of its outstanding material properties including an extremely wide bandgap (Eg ~4.9eV) and a high breakdown field (8 MV/cm). This review summarizes recent trends and progress in the growth/doping of β-Ga2O3 thin films and then offers an overview of the state-of-the-art in SBPD and FET devices. The present challenges for β-Ga2O3 devices to penetrate the market in real-world applications are also considered, along with paths for future work.
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- Award ID(s):
- 1748339
- PAR ID:
- 10060454
- Date Published:
- Journal Name:
- Proc. SPIE 10533, Oxide-based Materials and Devices IX
- Volume:
- 105330R
- Issue:
- 105330R
- Page Range / eLocation ID:
- 105330R
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Electronic devices get smaller and smaller in every generation. In micro-/nano-electronic devices such as high electron mobility transistors, heat dissipation has become a crucial design consideration due to the ultrahigh heat flux that has a negative effect on devices' performance and their lifetime. Therefore, thermal transport performance enhancement is required to adapt to the device size reduction. β-Ga2O3 has recently gained significant scientific interest for future power devices because of its inherent material properties such as extremely wide bandgap, outstanding Baliga's figure of merit, large critical electric field, etc. This work aims to use a machine learning approach to search promising substrates or heat sinks for cooling β-Ga2O3, in terms of high interfacial thermal conductance (ITC), from large-scale potential structures taken from existing material databases. With the ITC dataset of 1633 various substrates for β-Ga2O3 calculated by full density functional theory, we trained our recently developed convolutional neural network (CNN) model that utilizes the fused orbital field matrix (OFM) and composition descriptors. Our model proved to be superior in performance to traditional machine learning algorithms such as random forest and gradient boosting. We then deployed the CNN model to predict the ITC of 32 716 structures in contact with β-Ga2O3. The CNN model predicted the top 20 cubic and noncubic substrates with ITC on the same level as density functional theory (DFT) results on β-Ga2O3/YN and β-Ga2O3/MgO interfaces, which has the highest ITC of 1224 and 1211 MW/m2K, respectively, among the DFT-ITC datasets. Phonon density of states, group velocity, and scattering effect on high heat flux transport and consequently increased ITC are also investigated. Moderate to high phonon density of states overlap, high group velocity, and low phonon scattering are required to achieve high ITC. We also found three Magpie descriptors with strong Pearson correlation with ITC, namely, mean atomic number, mean atomic weight, and mean ground state volume per atom. Calculations of such descriptors are computationally efficient, and therefore, these descriptors provide a new route for quickly screening potential substrates from large-scale material pools for high-performance interfacial thermal management of high-electron mobility transistor devices.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1117/12.2302471
