The current circumstances indeed took a toll on how research is carried out, not only on the Fun-COMP project collaborators, but on a global scale.
Nevertheless, we’ve been able to continue our work, solving or working around the various issues the social distancing compels to tackle.
The list of recent publications from the researchers collaborating to this project proofs the dedication and commitment to their work. We’d like to share their success, and congratulate with each one of them for their accomplishments.

A note of merit goes to Xuan and Johannes, which along with their research partners published extremely interesting results concerning the investigation of the potential and scalability of the Non-von Neumann optical unit cell.

Xuan’s work investigates the behavior and potential range of applications of the NvN unit cell on different photonic platforms: silicon, and silicon nitride. The results help to identify the issues and advantages deriving from the adoption of either of the concepts, which is of high importance for the foreseeable future of the research project.

See 10.1364/OPTICA.379228 to view the full manuscript.

Johannes’s work explores the scalability of the NvN unit cell as a memory platform towards realistic scenarios. In particular, the authors developed a test photonic device capable to store and retrieve in an all-optical fashion up to 512 bits, implementing 256 individually addressed unit cells.

See 10.1109/JSTQE.2019.2956871 for further details.

Here follows a shortlist of all the publications from Fun-COMP research collaborators since January 2020.

• Lugnan, Alessio, Andrew Katumba, Floris Laporte, Matthias Freiberger, Stijn Sackesyn, C. Ma, Emmanuel Gooskens, Joni Dambre, and Peter Bienstman. “Photonic neuromorphic information processing and reservoir computing.” APL Photonics 5, no. 2 (2020): 020901. DOI: 10.1063/1.5129762
• Harkhoe, Krishan, Guy Verschaffelt, Andrew Katumba, Peter Bienstman, and Guy Van der Sande. “Demonstrating delay-based reservoir computing using a compact photonic integrated chip.” Optics Express 28, no. 3 (2020): 3086-3096. DOI: 10.1364/OE.382556
• Faneca, Joaquin, Santiago G-C. Carrillo, Emanuele Gemo, Carlota Ruiz de Galarreta, Thalía Domínguez Bucio, Frederic Y. Gardes, Harish Bhaskaran, Wolfram HP Pernice, C. David Wright, and Anna Baldycheva. “Performance characteristics of phase-change integrated silicon nitride photonic devices in the O and C telecommunications bands.” Optical Materials Express 10, no. 8 (2020): 1778-1791. DOI: 10.1364/OME.10.001778
• Gemo, Emanuele, Sameer V. Kesava, Carlota Ruíz de Galarreta, Liam Trimby, Santiago G-C. Carrillo, Moritz Riede, Anna Baldycheva, Arseny M. Alexeev, and C. David Wright “Simple technique for determining the refractive index of phase-change materials using near-infrared reflectometry” Optical Material Express, vol. 10, no. 3 (2020): 1675-1686. DOI: 10.1364/OME.395353
• Li, Xuan, Nathan Youngblood, Zengguang Cheng, Santiago García-Cuevas Carrillo, Emanuele Gemo, Wolfram H.P. Penice, C. David Wright, and Harish Bhaskaran. “Experimental investigation of silicon and silicon nitride platforms for phase change photonic in-memory computing” Optica 7, no. 3 (2020): 218-225. DOI: 10.1364/OPTICA.379228
• Feldmann, Johannes, Nathan Youngblood, Xuan Li, C. David Wright, Harish Bhaskaran, and Wolfram HP Pernice. “Integrated 256 cell photonic phase-change memory with 512-bit capacity.” IEEE Journal of Selected Topics in Quantum Electronics 26, no. 2 (2019): 1-7. DOI: 10.1109/JSTQE.2019.2956871

CORDIS (Community Researchand Development Information Service) is the EU Commission dissemination portal, which contains the information related to the many EU-funded research projects.

A recent editorial focuses on the achievements of the Fun-COMP research team, highlighting the research project aims, challenges and progress. The article can be found at this link.

Among the research activities carried out for the Fun-COMP project, the simulation methodology is an invaluable tool to move steps towards a better understanding of the devices mechanisms and behaviour.

Simulation, or modeling, usually consists of the mathematical replica of a device, over which the known laws of physics are applied, and whose results are tested against the experimental data. The general purpose is dual: expand the knowledge over the physical mechanisms and material properties governing the device behaviour, and use this knowledge to infer any possible improvement without the immediate need of an experimental exploration.

Indeed, simulation is quite a broad term, encasing several different approaches and aims. For the Fun-COMP research we developed several different methodologies, two of these dedicated to tackle only the unit cell behavior. One is the Finite Element model, defined to explore and test the unit cell physical laws, and the second one is the Behavioral model, employed to predict larger scales integration.

Further information on the simulation methods is contained in this report.

“Researchers take a step towards light-based brain-like computing”

Congratulations to Johannes, Nathan, David, Harish and Wolfram, for their publication in Nature of the development of a brain-like optical computing chip.

A press release is available at this link, and the article can be found at this page.

There is also a nice “Nature News and Views” article highlighting the achievements of the paper at this page.

Many thanks to Dr. Abu Sebastian and Dr. Simon Stringer!

During the 1st of our programmed cross-disciplinary training session, they delivered two extremely interesting talks.

Dr. Sebastian is Principal Research Staff Member at IBM Zurich, particularly focused on neuromorphic and in-memory computing research. He explored the fundamentals and applications of phase-change memory devices.

Dr. Stringer leads the Oxford Centre for Theoretical Neuroscience and Artificial Intelligence, based within the Department of Experimental Psychology. He provided a picture of the modelling techniques in brain science, in particular tackling the “binding problem” embedded in spiking neural network.

Andrew Katumba and Peter Bienstman form Imec published their most recent work on Journal of Lightwave Technology, “A Neuromorphic Silicon Photonics Nonlinear Equalizer for Optical Communications with Intensity Modulation and Direct Detection” (link)