ChipAI Guest Editors of Optical Materials Express Feature

Antonio Hurtado from the University of Strathclyde, United Kingdom (Lead Editor) and Bruno Romeira from the International Iberian Nanotechnology Laboratory, Portugal are Guest Editors of OSA Publishing's Digital Library special issue entitled “Optical Materials Express Feature Issue - Emerging Optical Materials, Devices and Systems for Photonic Neuromorphic Computing".

The Optical Society (OSA) publishes high-quality, peer-reviewed articles in its portfolio of journals, which serve the full breadth of the optics and photonics community.

This feature issue will highlight recent advances in the field of neuromorphic photonics for light-enabled machine learning and AI systems. This includes advanced neuromorphic photonic devices for artificial optical neurons, synaptic devices, photonic memories, etc., built from key-enabling optical components such as lasers, LEDs, photodetectors, micro-resonators, modulators, and plasmonic elements. Emerging neuromorphic photonic architectures will also be highlighted including photonic (spiking/convolutional/feed-forward/recurrent) neural networks, integrated photonic-electronic neuromorphic processing systems, and photonic tensor-core systems, amongst others.

Topics to be covered include but are not limited to:

• Material platforms for photonic neuromorphic computing and/or synapses including but not limited to silicon photonics, phase-change materials, high-speed switchable electro-optic materials, plasmonic systems, 2D material heterostructures, active emitter and photodetector materials etc.

• Photonic architectures for non-volatile memories, in-memory computing and spiking-neuron systems

• All-optical implementations of neural networks based on e.g. fibre-optical systems, frequency combs, Mach-Zender-interferometer systems etc.

• Intelligent algorithms for nanophotonics and optical system designs

• Free space optics and optoelectronics for machine learning

• Photonic reservoir computing

• Optical Ising machines

More information can be found here.

New Paper on Bursting and Excitability in Neuromorphic Resonant Tunneling Diodes

Image: Three-dimensional bifurcation diagram illustrating the stable steady-state branch (solid blue line), unstable steady-state branches (dashed blue lines), stable limit-cycle branch (colored gradient surface), unstable limit-cycle branches (red surfaces), and limit-cycle folds (red curves) versus the input bias voltage V0 for R=1Ω and different values of μ: (a) μ=0.002Ω−1, (b) μ=0.05Ω−1, (c) μ=0.14Ω−1.

Neurons exhibit excitability, the dynamical property that is likewise key to biologically inspired artificial intelligence. The neuromorphic circuits proposed so far have been slow and power-hungry. Seeking a better architecture that supports spikes as information carriers, the authors look to resonant tunnelling diodes as excitable neuromorphic spike generators. These nonlinear quantum nanoelectronic elements can reach terahertz frequencies and may be coupled to nanolasers for all-optical data transmission. This study theoretically characterizes their spiking and bursting dynamics and may establish a basis for fast, minimal-power optoelectronic circuits for machine learning.

The full article can be found here.

Ignacio Ortega-Piwonka1,2, Oreste Piro1, José Figueiredo3, Bruno Romeira4, and Julien Javaloyes1,2,*

New Paper on the Efficient light extraction in subwavelength GaAs/AlGaAs nanopillars for nanoscale light-emitting devices

This work reports on high extraction efficiency in subwavelength GaAs/AlGaAs semiconductor nanopillars. We achieve up to 37-fold enhancement of the photoluminescence (PL) intensity from sub-micrometer (sub-µm) pillars without requiring back reflectors, high-Qdielectric cavities, nor large 2D arrays or plasmonic effects.

This is a result of a large extraction efficiency for nanopillars <500 nm width, estimated in the range of 33-57%, which is much larger than the typical low efficiency (∼2%) of micrometer pillars limited by total internal reflection. Time-resolved PL measurements allow us to estimate the nonradiative surface recombination of fabricated pillars.

We conclusively show that vertical-emitting nanopillar-based LEDs, in the best-case scenario of both reduced surface recombination and efficient light out-coupling, have the potential to achieve notable large external quantum efficiency (∼45%), whereas the efficiency of large µm-pillar planar LEDs, without further methods, saturates at ∼2%. These results offer a versatile method of light management in nanostructures with prospects to improve the performance of optoelectronic devices including nanoscale LEDs, nanolasers, single-photon sources, photodetectors, and solar cells.

The full article can be found here.

Bruno Romeira*, Jérôme Borme, Hélder Fonseca, João Gaspar, and Jana B. Nieder*, Optics Express, Vol. 28, Issue 22, pp. 32302-32315 (2020) 

Meet Ekaterina Malysheva, ChipAI PhD Researcher at the Eindhoven University of Technology, The Netherlands

What inspired you to pursue a career in science?
Curiosity has always been the main driving force in my life - "why" was most likely the main question of my life, and it still remains. In childhood, I learned that humanity does not know all the answers to "why" questions, but there are some people for whom the search for answers has become a profession. That inspires me in the same way as people are inspired by space or deep ocean explorers.

How did you get where you are in your career path?
I come from Novosibirsk, which is the largest city in Siberia. This place is known not only for its cold weather but also for its big scientific centre. There I received a bachelor's degree in semiconductor physics and moved to Moscow to continue my studies in a master's degree in photonics. There I discovered the area of integrated photonics and began to search for the opportunity to continue research in this direction. Out of all other options, ChipAI project’s idea really surprised me by its novelty, and I realized that I want to be a part of this team.

How would you explain the importance of your research to the ChipAI project to a non-scientific person?
Many people have heard about neural networks and how its application in our every-day life - from target advertising and chatbots on the internet to self-driving cars. However, computing systems that we are currently using are not very energy-efficient and fast in calculating large scale neural networks, which limits their scope. At the same time, the most energy-efficient neural system known to humans is the brain, and the fastest thing in the universe is light. The goal of ChipAI project is to combine these two concepts and create a human brain-inspired light-driven computing system for very efficient neural network calculation.

What inspires you about science?
Science does not ask you to believe in anything - just look with your own eyes. And no matter how long people are trying to understand nature, there will always be room for more research.

How do you see the future of your Research?
Photonics has the potential to become a new generation of electronics - making complex calculations faster and more accessible. But there are lots of unknown in this area - how to make devices on-chip very small and efficient, how to integrate components together, which architecture will provide the best performance. I think the future of my research is more in technology development and scalability of current structures and systems-on-chip.

Briefly, what is a ‘day in your life’?
I’m trying to wake up at 7 am every day to have some time for sport or a short walk in the morning. At 8-9 I’m starting to work - either at home or taking a bike ride to the laboratory. I like to work more in the laboratory - despite the fact it’s more unpredictable, you can always see physical results with your own eyes. During the day it’s always nice to have a break and talk with a colleague, even if it’s online. At 6 pm I’m finishing with work and starting evening activities - it can be archery, online dance classes or just cooking.

In the context of ChipAI please describe your experience in being involved in a European project.
Being part of a European project is a very fascinating experience - many researchers with different backgrounds and capabilities come together to work on a common goal. This opens up opportunities for a better understanding of the general problem, exchange of experience and keeping motivated while working altogether.

Ultrafast optical integration and pattern classification for neuromorphic photonics

Spiking VCSEL-neuron. (a) Illustration of a biological neuron. (b) Experimental setup used to investigate a spiking VCSEL-neuron under external optical injection of intensity-encoded stimuli. Optical fibre connections are shown in red and electrical connections in blue. (c) Idealistic depiction of the LIF model. Inputs injected into the device (In) are integrated (Int.), with a time constant decay, towards a threshold potential (red dotted line). When the threshold requirement is met, the system fires a spiking response (Out) and the potential reaches the reset value (dark red) before returning to its resting potential (light blue). (d) Flow diagram of the VCSEL-neuron. Optical injection is encoded with pre-weighted inputs. These are integrated over time in the VCSEL-neuron where a spike activation function thresholds inputs before firing.

In today’s data-driven world, the ability to process large data volumes is crucial. Key tasks, such as pattern recognition and image classification, are well suited for artificial neural networks (ANNs) inspired by the brain. Neuromorphic computing approaches aimed towards physical realizations of ANNs

have been traditionally supported by micro-electronic platforms, but recently, photonic techniques for neuronal emulation have emerged given their unique properties (e.g. ultrafast operation, large bandwidths, low cross-talk). Yet, hardware-friendly systems of photonic spiking neurons able to perform processing tasks at high speeds and with continuous operation remain elusive. This work provides a first experimental report of Vertical-Cavity Surface-Emitting Laser-based spiking neurons demonstrating different functional processing tasks, including coincidence detection and pattern recognition, at ultrafast rates. Furthermore, our approach relies on simple hardware implementations using off-the-shelf components. These results therefore hold exciting prospects for novel, compact and high-speed neuromorphic photonic platforms for future computing and Artificial Intelligence systems.

The ChipAI partner University of Strathclyde that published the paper “Ultrafast optical integration and pattern classification for neuromorphic photonics based on spiking VCSEL neurons” in Scientific Reports (Nature Publishing). The paper reports pattern recognition at sub-ns bit rates using a photonic spiking VCSEL-Neuron. These results hold exciting prospects of functional processing tasks in the context of high-speed neuromorphic photonic platforms for future computing and artificial intelligence systems.

You can read the full article here.

Three-dimensional cross-nanowire networks

The ChipAI partner University of Strathclyde that published the paper "Three-dimensional cross-nanowire networks recover full terahertz state" in Science. This is the result of a fantastic collaboration between Oxford University, the Australian National University, and the ChipAI partner University of Strathclyde. Although for a different application, the team uses one of the techniques of flexible microfabrication employed in the context of the ChipAi project for neuromorphic nanocircuits.

Terahertz radiation encompasses a wide band of the electromagnetic spectrum, spanning from microwaves to infrared light, and is a particularly powerful tool for both fundamental scientific research and applications such as security screening, communications, quality control, and medical imaging. Considerable information can be conveyed by the full polarization state of terahertz light, yet to date, most time-domain terahertz detectors are sensitive to just one polarization component. Here we demonstrate a nanotechnology-based semiconductor detector using cross-nanowire networks that records the full polarization state of terahertz pulses. The monolithic device allows simultaneous measurements of the orthogonal components of the terahertz electric field vector without cross-talk. Furthermore, we demonstrate the capabilities of the detector for the study of metamaterials.

The full article can be found here.

Interview with Bruno Romeira

Bruno Romeira joined the INL - International Iberian Nanotechnology Laboratory, Braga, Portugal, as a Marie Curie COFUND Research Fellow, and he is currently a Staff Researcher and a Coordinator of the EU-H2020-FET-OPEN Project ChipAI.

He received the Ph.D. degree (summa cum laude) in physics and the European Ph.D. degree from the University of Algarve, Faro, Portugal, jointly with the University of Glasgow, Glasgow, U.K., and the University of Seville, Seville, Spain, in 2012.

Bruno Romeira is also strongly involved in Arts and Science initiatives at INL - International Iberian Nanotechnology Laboratory and is currently working with Mathew Biederman in a Vertigo STARTS Artistic Residency - the Mark II spiking perceptron | Reimagining Rosenblatt’s Perceptron through neuromorphic light based computation.

At INL since 2017, how was your journey before arriving here, and what were the most important projects you’ve worked in?

Before arriving at INL, I was a Marie Skłodowska-Curie Research Fellow at the Applied Physics Department and Institute for Photonic Integration of the Eindhoven University of Technology (2015–2017), where I worked towards the physical understanding and the development of minuscule light sources, including ultrasmall LEDs and lasers.

I arrived at INL in late 2017 as a Marie Curie COFUND Research Fellow and I am currently Staff Researcher at the Ultrafast Bio- and Nanophotonics group and coordinator of the H2020-FET-OPEN project “ChipAI”. Since my arrival at INL, I continued my work towards the development of novel minuscule LED sources. However, now my focus has been to use inspiration from the brain’s synaptic connectivity to develop neuron-like LEDs capable of learning tasks of interest for artificial intelligence technologies.

You have been involved in several initiatives and programs that put Arts and Science side by side, how do you see these worlds work together?

In the context of Arts and Science, I had the opportunity to share my research experience and showcase my research activities with a few artists at INL. I soon realized how artists are interested in the process of making science and in the fine technical details that lead to a given result or scientific breakthrough. Therefore, bringing two different communities together, with very different backgrounds, can help both artists and scientists to communicate better their achievements to society.

About this experience in particular, with MARK II project, what has been the impact in you, in your work and your career?

Within the MARK II project, I had the opportunity to showcase the results of our ongoing project related to the development of neuron-like LEDs capable of learning tasks of interest for artificial intelligence technologies. This helped me to improve my skills in communicating my research results to a different community. Therefore, these type of experiences can help me as a scientist to communicate better my scientific achievements to the society, for example in outreach activities.