Nanoscale Memristor Circuits and Systems

With the end of Moore's Law approaching quickly, mainstream CMOS downscaling is slowing down. Novel nanoscale emerging devices compatible with CMOS fabrication technologies promise to overcome this slow down. Ultra-dense multi-layer fabrics of nano-scale devices can be fabricated as BEOL (back end of line) on top of CMOS substrates. One of these emerging devices are memristors, also called resistive-RAM (RRAM), which are two-terminal devices whose resistance can be changed as the devices are stimulated differently. Some of these memristors allow for two-state resistances, while other less developed may allow for continuous non-volatile analog memory states. In this research line our main focus is to exploit these novel memristive devices combined with optimized CMOS circuits to provide ultra-compact ultra-low-power computing architectures for edge and IoT applications.

Main recent activities in this line include:

  • Design and fabrication of monolithic CMOS/memristor Proof-of-Concept computing systems using TiO RRAM Filamentary Memristors.
  • Computation of Spike-Time-Dependent-Plasticity Learning Rules with Memristors.
  • Stochastic Binary Spike-Time-Dependent-Plasticity for Memristor-based 1-bit weight learning and inference.
  • Calibration Techniques for ultra-low-voltage memristive read-out circuits.
Illustration of massive computing architectures of monolithic CMOS/Memristor neural computing chips assembled on dedicated PCBs.
Photograph of CMOS chip with memristor test devices fabricated on top.


Bernabé Linares Barranco >
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Teresa Serrano Gotarredona >
Google Scholar

Luis A. Camuñas Mesa >
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Group of Neuromorphic Systems


  • RRAM (Resistive RAM)
  • Non-volatile memristor memory
  • Nanoscale memristors
  • TiO filamentary memristors
  • 1T1R memristor crossbars
  • Spiking neuromorphic computing with memristors
  • Hopfield Neural Networks with memristors
  • Spike-Timing-Dependent-Plasticity with memristors

Research Highlights

L. A. Camuñas-Mesa, B. Linares-Barranco and T. Serrano-Gotarredona, "Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations", Materials, vol. 12, no. 7, article 2745, 2019  »  doi

B. Linares-Barranco, "Memristors fire away", Nature Electronics, vol. 1, no. 2, pp 100-101, 2018  »  doi

X. Guo, F. Merrikh-Bayat, L. Gao, B.D. Hoskins, F. Alibart, B. Linares-Barranco, L. Theogarajan, C. Teuscher and D.B. Strukov, "Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits", Frontiers in Neuromorphic Engineering, Frontiers in Neuroscience, vol. 9, article 488, 2015  »  doi

G. Indiveri, B. Linares-Barranco, R. Legenstein, G. Deligeorgis and T. Prodromakis, "Integration of nanoscale memristor synapses in neuromorphic computing architectures", Nanotechnology, vol. 24, no. 38, article 384010, 2013  »  doi

C. Zamarreño-Ramos, L. A. Camuñas-Mesa, J.A. Perez-Carrasco, T. Masquelier, T. Serrano-Gotarredona and B. Linares-Barranco, "On Spike-Timing-Dependent-Plasticity, Memristive Devices, and building a Self-Learning Visual Cortex", Frontiers in Neuromorphic Engineering, Frontiers in Neuroscience, vol. 5, article 26, 2011  »  doi

Key Research Projects & Contracts

Nano-Mind: Neuromorphic Perception and Nano-Memristive Cognition for High-Speed Robotic Actuation
PI: Teresa Serrano Gotarredona
Funding Body: Min. de Ciencia e Innovación
Jun 2020 - May 2024

MeM-Scales: Memory technologies with multi-scale time constants for neuromorphic architectures
PI: Bernabé Linares Barranco
Funding Body: European Union
Jan 2020 - Dec 2022

HERMES: Hybrid Enhanced Regenerative Medicine Systems
PI: Teresa Serrano Gotarredona
Funding Body: European Union
Jan 2019 - Dec 2022

NeuRAM3: NEUral computing aRchitectures in Advanced Monolithic 3D-VLSI nano-technologies
PI: Teresa Serrano Gotarredona
Funding Body: European Union
Jan 2016 - Jun 2019

MemoCiS: Memristors - Devices, Models, Circuits, Systems and Applications
PI: Bernabé Linares Barranco
Funding Body: COST Action IC1401
May 2014 - May 2018

All Research Areas & Lines