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.

Contact

Bernabé Linares Barranco<  >
Google Scholar

Teresa Serrano Gotarredona<  >
Google Scholar

Luis A. Camuñas Mesa<  >
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Group of Neuromorphic Systems

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

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