In this article, N-polar GaN-on-sapphire deep-recess metal-insulator-semiconductor (MIS)-highelectron-mobility transistors (HEMTs) with a breakthrough performance at W-band are presented. Compared with prior N-polar GaN MIS-HEMTs, a thin GaN cap layer and atomic layer deposition (ALD) ruthenium (Ru) gate metallization were used along with high-quality GaN-on-sapphire epitaxy from Transphorm Inc. Before SiN passivation, 94 GHz large signal load-pull shows that the transistor obtains a recordhigh 9.65 dB linear transducer gain and demonstrated 42% power-added efficiency (PAE) with associated 4.4 W/mm of output power density at 12 V drain bias. By biasing the drain at 8 V, the device shows an even higher PAE of 44% with an associated 2.6 W/mm of output power density. After SiN passivation, the fabricated N-polar GaN-on-sapphire HEMTs show a high PAE of 40.2% with an associated 4.85 W/mm of output power density. Furthermore, a very high output power density of 5.83 W/mm with 38.5% PAE is demonstrated at a 14 V drain bias. This power performance shows significant efficiency improvement over previous N-polar GaN-on-SiC and demonstrates a combined efficiency and power density beyond what has been reported for Ga-polar devices, in spite of the low-thermal-conductivity sapphire substrate. This shows that N-polar GaN-on-sapphire technology is an attractive candidate for millimeter-wave power amplifier applications with simultaneous high efficiency and power density.
This paper presents an approach to the analysis of on‐chip integrated spiral inductors in terms of parametric variabilities. An enhanced single‐π model is proposed to compensate for high frequency deviations. In addition to the single‐π model parameters, the skin effect and the proximity effect are included in the surrogate model to reflect high frequency behavior much more accurately while retaining a simple structure. Y‐parameters of the equivalent circuit are derived to extract the frequency‐dependent inductance and quality factor representations using the shunt and differential configurations. This study introduces a novel algorithm able to yield model parameter values through iterations converging on the predetermined inductance curve within an error margin of 5%. High‐Q and low‐Q characteristics of inductors arising from process variations were also captured with the proposed algorithm, along with the failure points of the inductance, the quality factor, and the self‐resonance frequency. Variability analysis results demonstrate that the differential configuration is more robust to inductance and quality factor failures, but also that it is more vulnerable to self‐resonance frequency failures.
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