NanoPico Photonic Computing

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:

1. Nano- and pico-photonics
2. Topological photonics
3. Neuromorphic photonics
4. Nanostructuring technologies
5. Super-resolution imaging

Student projects: Internships, research labs and Master's theses

We offer experimental and numerical projects in photonic computing and 3D nanofabrication. Each topic can be adapted to an internship, research-lab project or Master’s thesis:

• Reconfigurable Free-Space Diffractive Neural Networks
  Design optical neural networks in Python and implement them using spatial light modulators or digital micromirror devices. Explore adaptation to new tasks while preserving learned capabilities through selective layer updates. Flexible directions include deeper architectures, alternative propagation geometries, hardware-aware training, calibration and misalignment compensation, and reusable modular layouts.
• Hybrid Optical–Digital Neural Networks: Finding the Optimal Division of Computation
  Investigate how optical processing and digital neural networks can work together. Identify architectures that balance accuracy, robustness and computational cost.
• Fabrication-Aware Design of 3D-Printed Diffractive Neural Networks
  Incorporate realistic fabrication constraints into the design of optical neural networks. Explore how feature size, material properties and printing imperfections affect simulated and experimental performance.
• Free-Space Photonic Ising Machines
  Explore how optical interference and programmable light patterns can help solve combinatorial optimization problems. Develop numerical models, with the possibility of extending them to an experimental implementation.
• Exploring the Resolution Limits of Two-Photon Polymerization: Calibration and Fabrication with Custom and Commercial Systems
  Investigate how laser exposure, writing strategies and materials determine the smallest reproducible features in 3D printing. Develop fabrication protocols for photonic crystals and metamaterials.
• Compact Diffractive Computing with Nonlocal Fabry-Pérot Thin Films
  Investigate whether light coupling within thin optical cavities can replace free-space propagation in diffractive neural networks. Explore routes towards compact optical processors integrated with image sensors.

Interested in joining us? Please contact elena.goi@uni-jena.de to discuss a suitable project.

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.