Jianhua Joshua Yang

Professor

University of Southern California

United States

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Jianhua Joshua Yang is a Professor at the University of Southern California, specializing in neuromorphic engineering, memristive materials, and device applications. His recent research focuses on real-time signal processing, synaptic resistor circuits, and the development of analog computing systems. He has published extensively on various aspects of memristive technology, highlighting critical species for resistive switching and advances in memristive devices and systems.

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Collaborators (14)

Fuxi Cai

University of Michigan

UNITED STATES

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D. B. Strukov

UNITED STATES

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Xinyuan Zhang

Massachusetts Institute of Technology

UNITED STATES

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Ji-Hoon Kang

Assistant Professor

Inha University

SOUTH KOREA

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Sune Hannibal Holm

Københavns Universitet

DENMARK

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Linda Katehi

Professor

Texas A&M University

UNITED STATES

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Miao Hu

Assistant professor

Binghamton University

UNITED STATES

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Hefei Liu

Harvard University

UNITED STATES

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Stan Williams

Professor

Texas A&M University

UNITED STATES

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Nini Pryds

Technical University of Denmark

DENMARK

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Roberto Galeazzi

Technical University of Denmark

DENMARK

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Suin Yi

Assistant Professor

Texas A&M University

UNITED STATES

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Martin Salinga

Professor

University of Münster

GERMANY

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Silvia Tolu

Technical University of Denmark

DENMARK

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Jianhua Joshua Yang

Research Interests

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About

Articles (20)

HfZrO-based synaptic resistor circuit for a Super-Turing intelligent system

Real-Time Signal Processing Enabled By Fused Networks On A Memristor-Based System-On-Chip

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

Imperfection-enabled memristive switching in van der Waals materials

Memristive Field-Programmable Analog Arrays for Analog Computing

Neuromorphic Engineering: From Materials to Device Application

Parallel synaptic design of ferroelectric tunnel junctions for neuromorphic computing

Protons: Critical Species for Resistive Switching in Interface-Type Memristors

Thousands of conductance levels in memristors integrated on CMOS

2022 Roadmap on Neuromorphic Computing and Engineering

A Low-Current and Analog Memristor with Ru as Mobile Species

A Memristor with Low Switching Current and Voltage for 1S1R Integration and Array Operation

An artificial spiking afferent nerve based on Mott memristors for neurorobotics

Editorial Special Issue on 'Memory Devices and Technologies for the Next Decade'

Fully hardware-implemented memristor convolutional neural network

Integration and Co-design of Memristive Devices and Algorithms for Artificial Intelligence

Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks

Power-efficient neural network with artificial dendrites

Resistive switching materials for information processing

Roadmap on emerging hardware and technology for machine learning

Collaborators (14)

Fuxi Cai

D. B. Strukov

Xinyuan Zhang

Ji-Hoon Kang

Sune Hannibal Holm

Linda Katehi

Miao Hu

Hefei Liu

Stan Williams

Nini Pryds

Roberto Galeazzi

Suin Yi

Martin Salinga

Silvia Tolu

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