Engineering topics
Cui, Peng
Publications and source records attributed to Cui, Peng.
High-performance HZO/InAlN/GaN MISHEMTs for Ka-band application
This paper reports on the demonstration of microwave power performance at 30 GHz on InAlN/GaN metal–insulator–semiconductor high electron mobility transistor (MISHEMT) on silicon substrate by using the Hf 0.5 Zr 0.5 O 2 (HZO) as a gate dielectric. Compared with Schottky gate HEMT, the MISHEMT with a gate length (L G ) of 50 nm presents a significantly enhanced performance with an ON/OFF current ratio (I ON /I OFF ) of 9.3 × 10 7 , a subthreshold swing of 130 mV dec -1 , a low drain-induced barrier lowing of 45 mV V -1 , and a breakdown voltage of 35 V. RF characterizations reveal a current gain cutoff frequency (f T ) of 155 GHz and a maximum oscillation frequency (f max ) of 250 GHz, resulting in high (f T × f max ) 1/2 of 197 GHz and the record high Johnson's figure-of-merit (JFOM = f T × BV) of 5.4 THz V among the reported GaN MISHEMTs on Si. Further, the power performance at 30 GHz exhibits a maximum output power of 1.36 W mm -1 , a maximum power gain of 12.3 dB, and a peak power-added efficiency of 21%, demonstrating the great potential of HZO/InAlN/GaN MISHEMTs for the Ka-band application.
Improving the short-wave infrared response of strained GeSn/Ge multiple quantum wells by rapid thermal annealing
In this work, the evolution of structural, optical and optoelectronic properties of coherently strained Ge 0.883 Sn 0.117 /Ge multiple quantum wells (MQWs) grown by molecular beam epitaxy under rapid thermal annealing (RTA) is systematically investigated. The MQW structure remains fully-strained state with RTA at 400 °C or below and disrupts at higher annealing temperatures due to Sn segregation and interdiffusion of Ge and Sn atoms. The GeSn well layers exhibit the strongest absorption in 2.0–2.4 μm after annealing at 400 °C and become transparent above 1.8 μm after RTA at 600 °C or beyond due to serve Sn segregation. Owing to improved crystal quality after RTA at 400 °C, the dark current of the fabricated metal-semiconductor-metal photodetector is effectively lowered by more than two times. Additionally, the responsivities at 1.55 and 2.0 μm are improved by 4.15 and 3.78 folds, respectively, compared to those of the as-grown sample. Here, the results can be an insightful guidance for the development of high-performance short-wave infrared photonic devices based on Sn-containing group-IV low-dimensional structures.
Crystallography companion agent for high-throughput materials discovery
The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone and impossible to scale. With the advent of autonomous robotic scientists or self-driving laboratories, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which outputs probabilistic classifications—rather than absolutes—to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering substantial time savings. It is demonstrated on a diverse set of organic and inorganic materials characterization challenges. This method is directly applicable to inverse design approaches and robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.
bnl/pub-Maffettone_2020_08
The application of the XCA package as first demonstrated in aXiv:2008.00283. ABSTRACT: The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time consuming, error prone, and impossible to scale. With the advent of autonomous robotic scientists or self-driving labs, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which output probabilistic classifications --- rather than absolutes --- to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering unprecedented time savings, and is demonstrated on a diverse set of organic and inorganic materials challenges. This innovation is directly applicable to inverse design approaches, robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.