• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • About REMIND
  • Research
  • Publications
  • People
  • News
  • Opportunities
  • Contact Us

Reconfigurable Electronic Materials Inspired by Nonlinear Neuron Dynamics

Texas A&M University College of Engineering

Tunable stochastic memristors for energy-efficient encryption and computing

Kyung Seok Woo, Janguk Han, Su-in Yi, Luke Thomas, Hyungjun Park, Suhas Kumar, Cheol Seong Hwang

Tunable stochastic memristors for energy-efficient encryption and computing

April 15, 2024

Information security and computing, two critical technological challenges for post-digital computation, pose opposing requirements – security (encryption) requires a source of unpredictability, while computing generally requires predictability. Each of these contrasting requirements presently necessitates distinct conventional Si-based hardware units with power-hungry overheads. This work demonstrates Cu0.3Te0.7/HfO2 (‘CuTeHO’) ion-migration-driven memristors that satisfy the contrasting requirements. Under specific operating biases, CuTeHO memristors generate truly random and physically unclonable functions, while under other biases, they perform universal Boolean logic. Using these computing primitives, this work experimentally demonstrates a single system that performs cryptographic key generation, universal Boolean logic operations, and encryption/decryption. Circuit-based calculations reveal the energy and latency advantages of the CuTeHO memristors in these operations. This work illustrates the functional flexibility of memristors in implementing operations with varying component-level requirements.

Google Scholar link

View all publications on our Google Scholar profile.

Recent Publications

  • Tuning the Spin Transition and Carrier Type in Rare-Earth Cobaltates via Compositional Complexity
  • Revealing Complete Atomic-Scale Switching Pathways in van der Waals Ferroelectrics
  • Graphlet Decomposition Using Random-Walk Memristors
  • Origin of Stabilization of Ligand-Centered Mixed Valence Ruthenium Azopyridine Complexes: DFT Insights for Neuromorphic Applications
  • Mechanisms enabling reconfigurability and long-term retention in vanadium oxide electrochemical memory

© 2016–2026 Reconfigurable Electronic Materials Inspired by Nonlinear Neuron Dynamics Log in

Texas A&M Engineering Experiment Station Logo
  • College of Engineering
  • twitter
  • State of Texas
  • Open Records
  • Risk, Fraud & Misconduct Hotline
  • Statewide Search
  • Site Links & Policies
  • Accommodations
  • Environmental Health, Safety & Security
  • Employment