Neuromorphic devices & systems

Technologies for computing tomorrow’s AI

Neuromorphic computing and AI

Microelectronics and computers have revolutionized our way of life. Massive integration of semiconductor devices, fueled by an incredible stream of materials innovation, has provided us with tools to connect, sense, analyze, control, produce and make decisions in completely new ways.

Artificial intelligence (AI) is the ability to perform tasks that are generally associated with intelligent beings. Recently, bio- and neuro-inspired (neuromorphic) algorithms have attracted considerable attention with their ability to extract structure and knowledge from huge unstructured data sets by relying solely on limited domain expert knowledge. It will have an even greater impact on our way of life than the invention of the Internet.

To execute these new algorithms efficiently at the large scale required in datacenters or, for example, to interpret sensor data locally in embedded, very low-power solar-powered devices, we need novel neuromorphic compute architectures and hardware.

The Neuromorphic Devices & Systems group has a strong and long-standing track record in compute-system architectures and semiconductor materials and devices, which we are applying today to solve the computational challenges of neuromorphic and AI computing.

Our mission

The goal of our research is to develop new materials and devices for electronic and photonic neuromorphic computing systems, to invent processes and technologies to fabricate such devices and circuits, and to demonstrate power-efficient neuromorphic computing hardware.





Dr. Bert Jan Offrein
Neuromorphic Systems & Devices group

Dr. Valeria Bragaglia
Neuromorphic Devices & Materials
Research Staff Member

Dr. Jonas Weiss
Neuromorphic Architectures & Systems
Research Staff Member

Dr. Antonio La Porta
Neuromorphic Devices & Integration
Research Staff Member

Dr. Folkert Horst
Optical Neuromorphic Computing
Research Staff Member

Daniel Jubin
Processing & Assembly

Heinz Siegwart
Processing & Assembly

Dr. Laura Bégon-Lours
Neuromorphic Materials & Devices
Post-doctoral researcher

Youri Popoff
Neuromorphic Devices & Materials
PhD student

Felix Hermann
Optical Neuromorphic Computing
PhD student

Tommaso Stecconi
Neuromorphic Devices, Integration & Processing
PhD student

Elger Anne Vlieg
Optical Neuromorphic Computing
PhD student

Jacqueline Geler Kremer
Optical Neuromorphic Computing
PhD student

Mattia Halter
Neuromorphic Devices, Integration & Processing
PhD student


Ask the expert


EU projects


Bert Jan Offrein

Bert Jan Offrein
Manager Neuromorphic Devices & System group


BEOL technology platform based on ferroelectric synaptic devices for advanced neuromorphic processors

Directly Modulated Lasers on Silicon

Materials for Neuromorphic Circuits

Memory technologies with multi-scale time constants for neuromorphic architectures

Neuro-augmented 112Gbaud CMOS plasmonic transceiver platform for Intra- and Inter-DCI applications

Photonic Enables Petascale In-Memory Computing with Femtojoule Energy Consumption

PHotonic REServoir Computing

Wafer-scale, CMOS integration of photonics, plasmonics and electronics devices for mass manufacturing 200Gb/s NRZ transceivers towards low-cost Terabit connectivity in Data Centers

Energy- and Size-efficient Ultra-fast Plasmonic Circuits for Neuromorphic Computing Architectures

New generation of scientific, industrial leaders in the digital age

Ultra Low Power Event-Based Camera

SNF projects

Advanced Learning Methods On Dedicated nano-Devices

Novel Architectures for Photonic Reservoir Computing





  1. Muller, L. et al.
    Neuromorphic Systems Design by Matching Inductive Biases to Hardware Constraints,” 
    Front. in Neurosci. 14, 437 (2020).
  2. Stark, P. et al.
    Opportunities for integrated photonic neural networks,” 
    Nanophotonics 9(13), 4221-4232 (2020).
  3. Gokmen, T., Vlasov, Y.
    Acceleration of deep neural network training with resistive cross-point devices: Design considerations,” 
    Front. Neurosci. 10, 1–13 (2016).
  4. Merolla, P. et al. 
    A million spiking-neuron integrated circuit with a scalable communication network and interface,” 
    Science 345, 668–673 (2014).
  5. Vandoorne, K. et al. 
    Experimental demonstration of reservoir computing on a silicon photonics chip,” 
    Nat. Commun.
     5, 3541 (2014).


  1. Bégon-Lours, L. et al.
    Analog Resistive Switching in BEOL, Ferroelectric Synaptic Weights,” 
    IEEE Journal of the Electron Devices Society (2021).
  2. Bégon-Lours, L. et al.
    Ferroelectric, Analog Resistive Switching in Back‐End‐of‐Line Compatible TiN/HfZrO4/TiOx Junctions,” 
    PSS (RRL), 2000524 (2021).
  3. Halter, M. et al.
    Back-End, CMOS-Compatible Ferroelectric Field-Effect Transistor for Synaptic Weights,” 
    ACS Appl. Mater. Interfaces 12, 17725 (2020).
  4. Abel, S. et al.
    Multi-level optical weights in integrated circuits,” 
    in Proc. International Conference of Rebooting Computing (2017).
  5. Messner, A. et al.
    Integrated ferroelectric plasmonic optical modulator,” 
    in Proc. 2017 Optical Fiber Communications Conference and Exhibition (OFC), pp. 7–9 (2017).
  6. Abel, S. et al. 
    A hybrid barium titanate–silicon photonics platform for ultraefficient electro-optic tuning,” 
    J. Light. Technol. 34, 1688–1693 (2016).
  7. Hofrichter, J. et al. 
    A mode-engineered hybrid III-V-on-silicon photodetector,”
    in Proc. European Conference on Optical Communication (ECOC), pp. 1-3 (2015).

Materials & Integration

  1. Eltes, F. et al.
    A novel 25 Gbps electro-optic Pockels modulator integrated on an advanced Si photonic platform,” 
    in Proc. IEEE International Electron Devices Meeting (IEDM), pp. 601-604 (2017).
  2. Kormondy, K.J. et al.
    Microstructure and ferroelectricity of BaTiO3 thin films on Si for integrated photonics,” 
    Nanotechnology 28, 75706 (2017).
  3. Eltes, F. et al.
    Low-Loss BaTiO3–Si Waveguides for Nonlinear Integrated Photonics,” 
    ACS Photonics 3, 1698 (2016).
  4. Xiong, C. et al.
    Active silicon integrated nanophotonics: ferroelectric BaTiO3 devices,”
    Nano Lett. 14, 1419–1425 (2014).