Stan Williams

Professor

Texas A&M University

United States

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Stan Williams is a Professor at Texas A&M University in the United States. His research focuses on advanced materials and their applications in neuromorphic computing, including synaptic resistor circuits and memristor models. He has published recent articles on topics such as electrically-driven metal-insulator transitions and materials selection principles for electro-thermal neurons.

Articles (27)

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2022

Collaborators (6)

Damien Thompson

Professor

University of Limerick

IRELAND

View details

Miguel Muñoz Rojo

University of Twente

NETHERLANDS

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Thirumalai Venkatesan

Director and Professor

University of Oklahoma

UNITED STATES

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Suin Yi

Assistant Professor

Texas A&M University

UNITED STATES

View details

Eric Pop

Professor

Stanford University

UNITED STATES

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Jianhua Joshua Yang

Professor

University of Southern California

UNITED STATES

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Stan Williams

Research Interests

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About

Articles (27)

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

Electrically-Driven Metal-Insulator Transitions Emerging from Localizing Current Density and Temperature

Materials Selection Principles for Designing Electro-Thermal Neurons

A Faithful and Compact Diffusive Memristor Model

Accurate compact nonlinear dynamical model for a volatile ferroelectric ZrO2 capacitor

Axon-like active signal transmission

Linear symmetric self-selecting 14-bit kinetic molecular memristors

Linear, symmetric, self-selecting 14-bit molecular memristors

Localized Conduction Channels in Memristors

Memristive Ion Dynamics to Enable Biorealistic Computing

Intrinsic and Extrinsic Factors Influencing the Dynamics of VO2 Mott Oscillators

Energy and Space Efficient Parallel Adder Using Molecular Memristors

Spectral Ranking in Complex Networks Using Memristor Crossbars

Activity-difference training of deep neural networks using memristor crossbars

Activity-difference training of deep neural networks using memristor crossbars

Intrinsic and Extrinsic Factors Influencing the Dynamics of VO2 Mott Oscillators

Synaptic Resistor Circuits Based on Al Oxide and Ti Silicide for Concurrent Learning and Signal Processing in Artificial Intelligence Systems

Author Correction: Charge disproportionate molecular redox for discrete memristive and memcapacitive switching

Charge disproportionate molecular redox for discrete memristive and memcapacitive switching (vol 15, pg 380, 2020)

Memristor-based Offset Cancellation Technique in Analog Crossbars

Neuromorphic Engineering: From Materials to Device Application

Analog Acceleration of the Power Method using Memristor Crossbars

Combinatorial Optimization in Hopfield Networks with Noise and Diagonal Perturbations

Electro-Thermal Characterization of Dynamical VO2 Memristors via Local Activity Modeling

Energy and Space Efficient Parallel Adder Using Molecular Memristors

Entrenching Decision Trees in a Robust Molecular Circuit Element

Molecular building blocks for non-linear circuits

Collaborators (6)

Damien Thompson

Miguel Muñoz Rojo

Thirumalai Venkatesan

Suin Yi

Eric Pop

Jianhua Joshua Yang

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