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Abstract This letter reports the demonstration and electrical characterization of high-voltage AlN metal-semiconductor field-effect transistors (MESFETs) on single-crystal AlN substrates. Compared with AlN MESFETs on foreign substrates, the AlN-on-AlN MESFETs showed high breakdown voltages of over 2 kV for drain-to-gate spacing of 15 μm and one of the highest average breakdown fields among reported AlN MESFETs. Additionally, the devices also exhibited decent drain saturation current and on/off ratio without complicated regrown or graded contact layers, which are several times higher than those of reported AlN-on-sapphire MESEFTs. This work is beneficial for the future development of ultrawide bandgap AlN power electronics. 

Abstract This letter reports the demonstration of lateral AlN Schottky barrier diodes (SBDs) on single-crystal AlN substrates by metalorganic CVD (MOCVD) with an ultra-low ideality factor (η) of 1.65, a high Schottky barrier height of 1.94 eV, a breakdown voltage (BV) of 640 V, and a record high normalizedBVby the anode-to-cathode distance. The device current was dominated by thermionic emission, while most previously reported AlN SBDs suffered from defect-induced current with higherη(>4). This work represents a significant step towards high-performance ultra-wide bandgap AlN-based high-voltage and high-power devices. 

Ultrawide bandgap β-(AlxGa1−x)2O3 vertical Schottky barrier diodes on (010) β-Ga2O3 substrates are demonstrated. The β-(AlxGa1−x)2O3 epilayer has an Al composition of 21% and a nominal Si doping of 2 × 1017 cm−3 grown by molecular beam epitaxy. Pt/Ti/Au has been employed as the top Schottky contact, whereas Ti/Au has been utilized as the bottom Ohmic contact. The fabricated devices show excellent rectification with a high on/off ratio of ∼109, a turn-on voltage of 1.5 V, and an on-resistance of 3.4 mΩ cm2. Temperature-dependent forward current-voltage characteristics show effective Schottky barrier height varied from 0.91 to 1.18 eV while the ideality factor from 1.8 to 1.1 with increasing temperatures, which is ascribed to the inhomogeneity of the metal/semiconductor interface. The Schottky barrier height was considered a Gaussian distribution of potential, where the extracted mean barrier height and a standard deviation at zero bias were 1.81 and 0.18 eV, respectively. A comprehensive analysis of the device leakage was performed to identify possible leakage mechanisms by studying temperature-dependent reverse current-voltage characteristics. At reverse bias, due to the large Schottky barrier height, the contributions from thermionic emission and thermionic field emission are negligible. By fitting reverse leakage currents at different temperatures, it was identified that Poole–Frenkel emission and trap-assisted tunneling are the main leakage mechanisms at high- and low-temperature regimes, respectively. Electrons can tunnel through the Schottky barrier assisted by traps at low temperatures, while they can escape these traps at high temperatures and be transported under high electric fields. This work can serve as an important reference for the future development of ultrawide bandgap β-(AlxGa1−x)2O3 power electronics, RF electronics, and ultraviolet photonics. 

Recent advances in the causal analysis can accelerate incident response time, but only after a causal graph of the attack has been constructed. Unfortunately, existing causal graph generation techniques are mainly offline and may take hours or days to respond to investigator queries, creating greater opportunity for attackers to hide their attack footprint, gain persistency, and propagate to other machines. To address that limitation, we present Swift, a threat investigation system that provides high-throughput causality tracking and real-time causal graph generation capabilities. We design an in-memory graph database that enables space-efficient graph storage and online causality tracking with minimal disk operations. We propose a hierarchical storage system that keeps forensically-relevant part of the causal graph in main memory while evicting rest to disk. To identify the causal graph that is likely to be relevant during the investigation, we design an asynchronous cache eviction policy that calculates the most suspicious part of the causal graph and caches only that part in the main memory. We evaluated Swift on a real-world enterprise to demonstrate how our system scales to process typical event loads and how it responds to forensic queries when security alerts occur. Results show that Swift is scalable, modular, and answers forensic queries in real-time even when analyzing audit logs containing tens of millions of events.