His work on Optoelectronics is typically connected to Band gap as part of general Photonics study, connecting several disciplines of science. His Optoelectronics study frequently draws connections between adjacent fields such as Band gap, Photonics and Black phosphorus. His multidisciplinary approach integrates Memristor and Memistor in his work. Qiangfei Xia performs multidisciplinary study on Memistor and Resistive random-access memory in his works. While working on this project, he studies both Resistive random-access memory and Memristor. Qiangfei Xia undertakes interdisciplinary study in the fields of Electrical engineering and Nanotechnology through his research. Qiangfei Xia undertakes interdisciplinary study in the fields of Nanotechnology and Electrical engineering through his research. In his works, he conducts interdisciplinary research on Artificial intelligence and Deep learning. Qiangfei Xia conducts interdisciplinary study in the fields of Deep learning and Artificial intelligence through his works.
His work on Nanotechnology is being expanded to include thematically relevant topics such as Layer (electronics). His Nanotechnology research extends to the thematically linked field of Layer (electronics). He conducted interdisciplinary study in his works that combined Optoelectronics and Optics. Qiangfei Xia conducts interdisciplinary study in the fields of Optics and Optoelectronics through his works. Memristor and Quantum mechanics are commonly linked in his work. Quantum mechanics is closely attributed to Memristor in his work. Electrical engineering is closely attributed to Voltage in his work. The study of Voltage is intertwined with the study of Resistive random-access memory in a number of ways. His Electrical engineering research extends to the thematically linked field of Resistive random-access memory.
Qiangfei Xia focuses mostly in the field of Dimension (graph theory), narrowing it down to topics relating to Pure mathematics and, in certain cases, Field (mathematics) and Von Neumann architecture. He frequently studies issues relating to Pure mathematics and Field (mathematics). Qiangfei Xia connects relevant research areas such as CMOS and Quantum tunnelling in the domain of Optoelectronics. His CMOS study frequently draws connections to adjacent fields such as Optoelectronics. His study on Neuroscience is interrelated to topics such as Afferent and Neuroprosthetics. His Afferent study frequently links to adjacent areas such as Neuroscience. His work often combines Memristor and Resistor studies. His multidisciplinary approach integrates Resistor and Memristor in his work. His research brings together the fields of Efficient energy use and Electrical engineering.
His Neuroscience study has been linked to subjects such as Neuroprosthetics and Afferent. Afferent is closely attributed to Neuroscience in his research. In most of his Electrical engineering studies, his work intersects topics such as Resistive touchscreen. As part of his studies on Resistive touchscreen, Qiangfei Xia often connects relevant subjects like Electrical engineering. He conducts interdisciplinary study in the fields of Artificial neural network and Neuromorphic engineering through his works. In his works, he performs multidisciplinary study on Neuromorphic engineering and Spiking neural network. Qiangfei Xia combines Spiking neural network and Artificial neural network in his research. Qiangfei Xia merges Electronic engineering with Memristor in his study. Qiangfei Xia integrates Memristor with Electronic engineering in his study.
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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)
Memristive crossbar arrays for brain-inspired computing
Qiangfei Xia;J. Joshua Yang.
Nature Materials (2019)
Memristor―CMOS Hybrid Integrated Circuits for Reconfigurable Logic
Qiangfei Xia;Warren Robinett;Michael W. Cumbie;Neel Banerjee.
Nano Letters (2009)
Analogue signal and image processing with large memristor crossbars
Can Li;Miao Hu;Miao Hu;Yunning Li;Hao Jiang.
Nature Electronics (2018)
Fully memristive neural networks for pattern classification with unsupervised learning
Zhongrui Wang;Saumil Joshi;Sergey Savel’ev;Wenhao Song.
Nature Electronics (2018)
Black Phosphorus Mid-Infrared Photodetectors with High Gain
Qiushi Guo;Andreas Pospischil;Maruf Bhuiyan;Hao Jiang.
Nano Letters (2016)
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)
Resistive switching materials for information processing
Zhongrui Wang;Huaqiang Wu;Geoffrey W. Burr;Cheol Seong Hwang.
Nature Reviews Materials (2020)
Black Phosphorus Radio-Frequency Transistors
Han Wang;Xiaomu Wang;Fengnian Xia;Luhao Wang.
Nano Letters (2014)
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