H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Electronics and Electrical Engineering D-index 39 Citations 8,152 93 World Ranking 1917 National Ranking 814

Overview

What is he best known for?

The fields of study he is best known for:

  • Electrical engineering
  • Semiconductor
  • Optoelectronics

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.

His most cited work include:

  • Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing (841 citations)
  • Memristor―CMOS Hybrid Integrated Circuits for Reconfigurable Logic (484 citations)
  • Analogue signal and image processing with large memristor crossbars (375 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Memristor (43.23%)
  • Optoelectronics (33.55%)
  • Nanotechnology (25.81%)

What were the highlights of his more recent work (between 2017-2021)?

  • Memristor (43.23%)
  • Artificial neural network (13.55%)
  • Neuromorphic engineering (13.55%)

In recent papers he was focusing on the following fields of study:

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.

Between 2017 and 2021, his most popular works were:

  • Analogue signal and image processing with large memristor crossbars (375 citations)
  • Fully memristive neural networks for pattern classification with unsupervised learning (321 citations)
  • Memristive crossbar arrays for brain-inspired computing (302 citations)

In his most recent research, the most cited papers focused on:

  • Electrical engineering
  • Semiconductor
  • Integrated circuit

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing

Zhongrui Wang;Saumil Joshi;Sergey E. Savel’ev;Hao Jiang.
Nature Materials (2017)

799 Citations

Memristor―CMOS Hybrid Integrated Circuits for Reconfigurable Logic

Qiangfei Xia;Warren Robinett;Michael W. Cumbie;Neel Banerjee.
Nano Letters (2009)

631 Citations

Black Phosphorus Mid-Infrared Photodetectors with High Gain

Qiushi Guo;Andreas Pospischil;Maruf Bhuiyan;Hao Jiang.
Nano Letters (2016)

417 Citations

Fully memristive neural networks for pattern classification with unsupervised learning

Zhongrui Wang;Saumil Joshi;Sergey Savel’ev;Wenhao Song.
Nature Electronics (2018)

392 Citations

Memristive crossbar arrays for brain-inspired computing

Qiangfei Xia;J Joshua Yang.
Nature Materials (2019)

367 Citations

Black Phosphorus Radio-Frequency Transistors

Han Wang;Xiaomu Wang;Fengnian Xia;Luhao Wang.
Nano Letters (2014)

327 Citations

Analogue signal and image processing with large memristor crossbars

Can Li;Miao Hu;Miao Hu;Yunning Li;Hao Jiang.
Nature Electronics (2018)

325 Citations

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)

324 Citations

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)

281 Citations

Anatomy of Ag/Hafnia-Based Selectors with 1010 Nonlinearity.

Rivu Midya;Zhongrui Wang;Jiaming Zhang;Sergey E. Savel'ev.
Advanced Materials (2017)

190 Citations

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Best Scientists Citing Qiangfei Xia

J. Joshua Yang

J. Joshua Yang

University of Southern California

Publications: 59

R. Stanley Williams

R. Stanley Williams

Texas A&M University

Publications: 52

Ye Zhou

Ye Zhou

Shenzhen University

Publications: 49

Dmitri B. Strukov

Dmitri B. Strukov

University of California, Santa Barbara

Publications: 42

Ming Liu

Ming Liu

Chinese Academy of Sciences

Publications: 41

Daniele Ielmini

Daniele Ielmini

Politecnico di Milano

Publications: 41

Han Zhang

Han Zhang

Shenzhen University

Publications: 40

Huaqiang Wu

Huaqiang Wu

Tsinghua University

Publications: 38

Wei Lu

Wei Lu

University of Michigan–Ann Arbor

Publications: 36

Bin Gao

Bin Gao

Tsinghua University

Publications: 33

Hangbing Lv

Hangbing Lv

Chinese Academy of Sciences

Publications: 33

John Paul Strachan

John Paul Strachan

Hewlett-Packard (United States)

Publications: 30

Shibing Long

Shibing Long

University of Science and Technology of China

Publications: 26

He Qian

He Qian

Tsinghua University

Publications: 25

Cheol Seong Hwang

Cheol Seong Hwang

Seoul National University

Publications: 25

Wei Wu

Wei Wu

University of Southern California

Publications: 25

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