NanoPico Photonic Computing
Dr. Elena GOI
Dr. Elena GOI, in the Nano Lab
Dr. Elena Goi.
Image: Nicole Nerger (University of Jena)Dr. Elena GOI
E-mail: elena.goi@uni-jena.de
Phone: +49 3641 9-47571
Dr. Elena Goi joined the Institute of Applied Physics and the Abbe Center of Photonics in July 2025 to establish the NanoPico Photonic Computing junior research group. Her research program is dedicated to the development of next-generation optical hardware capable of executing high-density, energy-efficient analog computing. Dr. Goi’s academic background is in solid state physics and she holds an M.Sc. from the University of Trieste, Italy. She was awarded a PhD from RMIT University, Australia, for her work on engineering high-refractive index three dimensional photonic crystals and topological photonic systems. From 2020 to 2025, she served as a Research Fellow and later as Associate Professor at the University of Shanghai for Science and Technology, China, specializing in optical and optoelectronic neuromorphic systems.
The NanoPico Photonic Computing group is supported by the BMFTR project, "PicPhotMat - Engineered materials for picophotonic analog computing". With this project, Dr. Goi follows an innovative approach of utilizing the interaction between structured light and engineered materials for optical computation, with the goal of overcoming current constraints in integration- and computational density. Contributing to the frontier research in picophotonics, Dr. Goi aims to reduce computational elements to the size of material lattice constants, achieving enhanced functionality and fundamentally higher density and scalability for optical analog computing hardware that may, for example, be used in the next generation of AI accelerators.
Research Areas
The main research areas of the NanoPico Photonic Computing Group include:
- Development of fast, scalable optical probing systems for material characterization with sub-nanometer resolution
- Design of 3D-printed, high-refractive-index functional metamaterials
- Exploration of new optical information processing schemes using programmable optical elements
- Study of light-matter interactions at the sub-nanometer and picometer scale
Scanning electron microscope (SEM) image (a) and optical microscope image (b) (top view) of a laser printed diffractive neural network. c) SEM image of a laser printed diffractive element.
Image: E. Goi, et al. Nature Communications 13, 7531 (2022)
Scanning electron microscopy (SEM) images of the (–101) surface of a Weil point photonic crystal
Image: E. Goi, et al. Laser and Photonic Reviews 12, 1700271 (2017)Teaching Fields
Dr. Goi's course lectures are focused on sharing core principles and advanced applications of photonics at various scales. Starting in the winter term 2025/26, she is teaching the course "Neuromorphic Photonics - Platforms & Applications".
Research Methods
The NanoPico Photonic Computing Group builds upon strong expertise across multiple areas of modern photonics:
- Nano- and pico-photonics
- Topological photonics
- Neuromorphic photonics
- Nanostructuring technologies
- Super-resolution imaging
Recent research results
Dr Goi’s background is nanophotonics, focusing on the study of the interaction of nanometer-scale objects with light, in particular dielectric 3D periodic structures called photonic crystals and metasurfaces (1, 2).
Over the past few years, Dr Elena Goi’s research has achieved several milestones that have advanced the frontier of neuromorphic photonics and optical computing. In a 2020 perspective article, she outlined a vision for photonic memristive neuromorphic computing, arguing that merging neuromorphic architecture with photonic interconnects can significantly enhance information-density, speed and energy-efficiency compared to purely electronic systems (3). In 2021, her team demonstrated nanoprinted high-neuron-density optical perceptrons directly on CMOS imaging chips, achieving neural densities exceeding 500 million neurons per cm² (4). This concept of optoelectronic information processing, where computation is carried out directly in the optical domain, was further extended in 2022, when she reported the first experimental realization of a compact optical-electronic module integrating multi-layer diffractive neural networks with CMOS sensors to directly retrieve Zernike-based pupil phase distributions from incident point-spread functions (5). These functional optoelectronic devices, which perform inference in the optical domain at the speed of light, enabled direct optical phase retrieval without any digital post-processing (6-8).
[1] Yu et al., "Neuron-inspired Steiner tree networks for three-dimensional low-density metastructures," Adv. Sci. 8, 2100141 (2021).
[2] Goi et al., “Observation of Type I photonic Weyl points in optical frequencies,” Laser & Photon. Rev. 12, 1700271 (2017).
[3] Goi et al., “Perspective on photonic memristive neuromorphic computing," PhotoniX, 1, 3 (2020).
[4] Goi et al., “Nanoprinted high neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip,” Light: Sci. & Appl. 10, 40 (2021).
[5] Goi et al., "Direct Zernike polynomial-phase retrieval with integrated diffractive deep neural networks,” Nature Commun. 13, 7531 (2022).
[6] Gu et al. (Eds.), “Neuromorphic Photonic Devices and Applications,”Paperback ISBN: 9780323988292 (2024).
[7] Goi et al., “Nanoprinted neural networks,” Roadmap on Neuromorphic Photonics, https://arxiv.org/abs/2501.07917External link (2025).
[8] Chen et al., "Quantitative comparison of the computational complexity of optical, digital and hybrid neural network architectures for image classification tasks," Opt. Express 31, 44474 (2023).
The fresh link to the Goi group at the IAP will appear here very soon.