Publications by authors named "Shenglei Zhao"

GaN-on-Si high-electron-mobility transistors have emerged as the next generation of high-powered and cost-effective microwave devices; however, the limited thermal conductivity of the Si substrate prevents the realization of their potential. In this paper, a GaN-on-insulator (GNOI) structure is proposed to enhance the heat dissipation ability of a GaN-on-Si HEMT. Electrothermal simulation was carried out to analyze the thermal performance of the GNOI-on-Si HEMTs with different insulator dielectrics, including SiO, SiC, AlN, and diamond.

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In this work, we demonstrated the epitaxial growth of a gallium nitride (GaN) buffer structure on 200 mm SOI (silicon-on-insulator) substrates. This epitaxial layer is grown using a reversed stepped superlattice buffer (RSSL), which is composed of two superlattice (SL) layers with different Al component ratios stacked in reverse order. The upper layer, with a higher Al component ratio, introduces tensile stress instead of accumulative compressive stress and reduces the in situ curvature of the wafer, thereby achieving a well-controlled wafer bow ≤ ±50 µm for a 3.

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Article Synopsis
  • * The threshold voltage maintains stability with a minimal variation of 0.1 V, and off-state leakage current only slightly increases under high temperatures (from 2.87 × 10 to 1.85 × 10 mA/mm).
  • * High temperatures introduce two trap states that slightly degrade performance, but overall, the findings support AlGaN HEMT's suitability for high-temperature applications, advancing the field of ultra-wide gap semiconductors. *
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Incipient fault detection in a hydraulic system is a challenge in the condition monitoring community. Existing research mainly monitors abnormal working conditions in hydraulic systems by separately detecting the key working parameter, which often causes a high miss warning rate for incipient faults due to the oversight of parameter dependence. A principal component analysis provides an effective method for incipient fault detection by taking the correlation of multiple parameters into consideration, but this technique assumes the systems are Gaussian-distributed, making it invalid for a dynamic non-Gaussian system.

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