High drain-current-density and high breakdown-field Al0.36Ga0.64N-channel heterojunction filed-effect transistors with a dual AlN/AlGaInN barrier layer

In this study, we fabricated and characterized heterojunction field-effect transistors (HFETs) based on an Al0.36Ga0.64N-channel heterostructure with a dual AlN/AlGaInN barrier layer. The device fabrication was accomplished by adopting a regrown n++-GaN layer for ohmic contacts. The fabricated HFETs with a gate length of 2 μm and a gate-to-drain distance of 6 μm exhibited an on-state drain current density as high as approximately 270 mA/mm and an off-state breakdown voltage of approximately 1 kV, which corresponds to an off-state critical electric field of 166 V/μm. This breakdown field, as a comparison in devices without field-plate electrodes, reaches approximately four-fold higher than that for conventional GaN-channel HFETs and was considered quite reasonable as an Al0.36Ga0.64N-channel transistor. It was also confirmed that the devices adopting the dual AlN/AlGaInN barrier layer showed approximately one order of magnitude smaller gate leakage currents than those for devices without the top AlN barrier layer.

T-Gate shaped AlN/β-Ga2O3 HEMT for RF and High Power Nanoelectronics

In this paper, we report record DC and RF performance in β-Ga₂O₃ High Electron Mobility Transistor (HEMT) with field-plate T-gate using 2-D simulations. The T gate with head-length L_HL of 180 nm and foot-length L_FL of 120 nm is used in the highly scaled device with an aspect ratio (L_G/t_barrier) of ~ 5. The proposed device takes advantage of a highly polarized Aluminum Nitride (AlN) barrier layer to achieve high Two-Dimensional Electron Gas (2DEG) density in the order of 2.3 × 10¹³ cm^-2, due to spontaneous as well as piezoelectric polarization components. In the depletion mode operation, maximum drain current I_D,MAX of 1.32 A/mm, and relatively flat transconductance characteristics with a maximum value of 0.32 S/mm are measured. The device with source-drain distance L_SD of 1.9 µm exhibits record low specific-on resistance R_ON,sp of 0.136 mΩ-cm^–2, and off-state breakdown voltage of 403 V, which correspond to the record power figure-of-merit (PFoM) of ~ 1194 MW/cm². Additionally, current gain cut-off frequency f_T and maximum oscillation frequency f_MAX of 48 and 142 GHz are estimated. The obtained results show the potential of Ga₂O₃ HEMT for futuristic power devices.

T-Gate shaped AlN/β-Ga2O3 HEMT for RF and High Power Nanoelectronics

In this paper, we report record DC and RF performance in β-Ga₂O₃ High Electron Mobility Transistor (HEMT) with field-plate T-gate using 2-D simulations. The T gate with head-length L_HL of 180 nm and foot-length L_FL of 120 nm is used in the highly scaled device with an aspect ratio (L_G/t_barrier) of ~ 5. The proposed device takes advantage of a highly polarized Aluminum Nitride (AlN) barrier layer to achieve high Two-Dimensional Electron Gas (2DEG) density in the order of 2.3 × 10¹³ cm^-2, due to spontaneous as well as piezoelectric polarization components. In the depletion mode operation, maximum drain current I_D,MAX of 1.32 A/mm, and relatively flat transconductance characteristics with a maximum value of 0.32 S/mm are measured. The device with source-drain distance L_SD of 1.9 µm exhibits record low specific-on resistance R_ON,sp of 0.136 mΩ-cm^–2, and off-state breakdown voltage of 403 V, which correspond to the record power figure-of-merit (PFoM) of ~ 1194 MW/cm². Additionally, current gain cut-off frequency f_T and maximum oscillation frequency f_MAX of 48 and 142 GHz are estimated. The obtained results show the potential of Ga₂O₃ HEMT for futuristic power devices.

Breakdown Voltage Enhancement of Al0.1Ga0.9N channel HEMT With Recessed Floating Field Plate

In this paper, electrical and microwave characteristics of Al0.1Ga0.9N channel HEMTs was reported. The device performance were evaluated for conventional gate, field plate gate, and recessed floating field plate with Silicon nitride (SiN)/Hafnium oxide (HfO2) passivation. The recessed floating field plate HEMT with gate length LG = 0.8 µm, gate to drain distance LGD = 1 µm, and HfO2(SiN) passivation HEMT reports peak drain current density (IDS) of 0.282(0.288) A/mm at VGS = 0V, three terminal off-state breakdown voltage (VBR) of 677 (617) V, 6.38 Ω.mm of ON-resistance (RON), transconductance (gm,max) of 93(95) mS/mm, and FT/FMAX of 11.4/49 (12/22) GHz. The HfO2 (SiN) passivation device demonstrated the Johnson figure of merit (JFoM)) of 7.71 (7.404) THz.V and FMAX x VBR product of 33.173 (13.574) THz.V. The high JFoM along with high FMAX x VBR indicates the potential of the ultrawide bandgap AlGaN HEMTs for future power switching and high-power microwave applications. The proposed device DC characteristics are validated with reported expeimental work, which shows similar IDS and 54% and and 31% improvement in breakdown voltage on comparisons with conventional HEMT.

AI and social theory

In this paper, we sketch a programme for AI-driven social theory. We begin by defining what we mean by artificial intelligence (AI) in this context. We then lay out our specification for how AI-based models can draw on the growing availability of digital data to help test the validity of different social theories based on their predictive power. In doing so, we use the work of Randall Collins and his state breakdown model to exemplify that, already today, AI-based models can help synthesise knowledge from a variety of sources, reason about the world, and apply what is known across a wide range of problems in a systematic way. However, we also find that AI-driven social theory remains subject to a range of practical, technical, and epistemological limitations. Most critically, existing AI-systems lack three essential capabilities needed to advance social theory in ways that are cumulative, holistic, open-ended, and purposeful. These are (1) semanticisation, i.e., the ability to develop and operationalize verbal concepts to represent machine-manipulable knowledge; (2) transferability, i.e., the ability to transfer what has been learned in one context to another; and (3) generativity, i.e., the ability to independently create and improve on concepts and models. We argue that if the gaps identified here are addressed by further research, there is no reason why, in the future, the most advanced programme in social theory should not be led by AI-driven cumulative advances.

Multipath Forecasting: the Aftermath of the 2020 American Crisis

Recent years have seen major political crises throughout the world. Most recently, the US was swept by a wave of protests, urban riots, and violent confrontations between left- and right-wing extremists. Understanding how future crises will unfold and assessing the resilience of different countries to various shocks is of foremost importance in averting the human costs of state breakdown and civil war. In a recent publication (Turchin et al. 2018) we proposed a novel transdisciplinary approach to modeling social breakdown, recovery, and resilience. This approach builds on recent breakthroughs in macrosocial dynamics (and specifically structural-demographic theory), statistical analysis of large-scale historical data, and dynamic modelling. Our main goal is to construct a series of probabilistic scenarios of social breakdown and recovery. We called this approach—similar to ensemble forecasting in weather prediction—multipath forecasting (MPF). In this article I develop a “prototype” of the MPF engine with the goal of illustrating the utility a fully developed version may have. I first apply the computational model to the period of American history from the beginning of the nineteenth to the end of the twentieth century, with the goal of parameterizing the model and testing it against data. Then I use the parameterized model to forecast the dynamics of instability in the USA beyond 2020 and illustrate how the MPF engine can be used to explore the effects of different policy interventions.