His scientific interests lie mostly in Memristor, Neuromorphic engineering, Optoelectronics, Nanotechnology and Crossbar switch. Computer architecture is closely connected to Artificial intelligence in his research, which is encompassed under the umbrella topic of Memristor. Qiangfei Xia interconnects Conduction channel and Dielectric in the investigation of issues within Neuromorphic engineering.
His Optoelectronics study incorporates themes from Fast switching and Electronics. His Nanotechnology research is multidisciplinary, incorporating perspectives in Pass transistor logic and Resistive random-access memory. His study in Crossbar switch is interdisciplinary in nature, drawing from both Nanowire, Electronic circuit and Test set.
Qiangfei Xia mainly focuses on Memristor, Optoelectronics, Nanotechnology, Nanoimprint lithography and Neuromorphic engineering. He combines subjects such as Artificial neural network, CMOS and Crossbar switch with his study of Memristor. His work deals with themes such as Layer, Thin film, Transistor and Optics, which intersect with Optoelectronics.
His Nanotechnology research integrates issues from Chemical engineering, Silicon and Unconventional computing. He works mostly in the field of Nanoimprint lithography, limiting it down to topics relating to Nanolithography and, in certain cases, Soft lithography. His Neuromorphic engineering research incorporates elements of Computer architecture and Electronics.
Qiangfei Xia mainly investigates Memristor, Artificial neural network, Neuromorphic engineering, Crossbar switch and Electronic engineering. The Memristor study combines topics in areas such as Transistor, Optoelectronics, CMOS and Electronic circuit. His work deals with themes such as Oxide and Resistive switching, which intersect with Optoelectronics.
Within one scientific family, he focuses on topics pertaining to Capacitive sensing under Artificial neural network, and may sometimes address concerns connected to Linearity, Spice and Electronics. His Neuromorphic engineering research is multidisciplinary, incorporating perspectives in Computer architecture, Silicon oxide and Scalability. His biological study spans a wide range of topics, including Computer hardware and Quantum tunnelling.
His primary areas of study are Memristor, Neuromorphic engineering, Crossbar switch, Computer architecture and Artificial neural network. His Memristor study which covers Artificial intelligence that intersects with Control engineering. Qiangfei Xia interconnects Resistive switching, Dielectric and System integration in the investigation of issues within Neuromorphic engineering.
His Crossbar switch research is multidisciplinary, relying on both NAND gate, Ampere and Nano-. His MNIST database study in the realm of Artificial neural network connects with subjects such as Matrix multiplication. His Electronic engineering research integrates issues from Transistor array and Hafnium oxide.
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Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
Zhongrui Wang;Saumil Joshi;Sergey E. Savel’ev;Hao Jiang.
Nature Materials (2017)
Memristor―CMOS Hybrid Integrated Circuits for Reconfigurable Logic
Qiangfei Xia;Warren Robinett;Michael W. Cumbie;Neel Banerjee.
Nano Letters (2009)
Black Phosphorus Mid-Infrared Photodetectors with High Gain
Qiushi Guo;Andreas Pospischil;Maruf Bhuiyan;Hao Jiang.
Nano Letters (2016)
Fully memristive neural networks for pattern classification with unsupervised learning
Zhongrui Wang;Saumil Joshi;Sergey Savel’ev;Wenhao Song.
Nature Electronics (2018)
Memristive crossbar arrays for brain-inspired computing
Qiangfei Xia;J Joshua Yang.
Nature Materials (2019)
Black Phosphorus Radio-Frequency Transistors
Han Wang;Xiaomu Wang;Fengnian Xia;Luhao Wang.
Nano Letters (2014)
Analogue signal and image processing with large memristor crossbars
Can Li;Miao Hu;Miao Hu;Yunning Li;Hao Jiang.
Nature Electronics (2018)
Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
Can Li;Daniel Belkin;Daniel Belkin;Yunning Li;Peng Yan;Peng Yan.
Nature Communications (2018)
Memristor-Based Analog Computation and Neural Network Classification with a Dot Product Engine.
Miao Hu;Catherine E. Graves;Can Li;Yunning Li.
Advanced Materials (2018)
Anatomy of Ag/Hafnia-Based Selectors with 1010 Nonlinearity.
Rivu Midya;Zhongrui Wang;Jiaming Zhang;Sergey E. Savel'ev.
Advanced Materials (2017)
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