We employ one- and two-photon polymerizatin, i.e. flash-TPP printing, which is compatible with metal-oxid-semiconductor (CMOS) technology, to fabricate polymer-cladded and single-mode 3D photonic waveguides and adiabatic splitters. Our 3D technology is a major step forward towards the highly-interconnection required in optical neural networks, which removes the high energy dissipation of electronics and where 3D integration enables scalability that is challenging to realize in 2D.

Photonic neural networks are a highly sought-after area of research due to their potential for high-performance complex computing. Unlike artificial neural networks, which use simple nonlinear maps, biological neurons transmit information and perform computations through spikes that depend on spike time and/or rate. Through comprehensive studies and experiments, a strong foundation has been laid for the development of photonic neural networks. We have recently developed a large-scale spiking neural network, consisting of more than 30.000 neurons, which serves as a proof-of-concept experiment for novel bio-inspired learning concepts. This breakthrough is significant because it demonstrates the potential of using photonic neural networks for advanced computing and highlights the importance of incorporating biological principles into artificial intelligence research.

Photonic computing has attracted much attention due to its great potential to accelerate artificial neural network operations. However, the processing of a large amount of data, such as image data, basically requires large-scale photonic circuits and is still challenging due to its low scalability of the photonic integration. Here, we propose a scalable image processing approach, which uses a temporal degree of freedom of photons. In the proposed approach, the spatial information of a target object is compressively transformed to a time-domain signal using a gigahertz-rate random pattern projection technique. The time-domain signal is optically acquired at a single-input channel and processed with a microcavity-based photonic reservoir computer. We experimentally demonstrate that this photonic approach is capable of image recognition at gigahertz rates.

We experimentally demonstrate an autonomous, fully tuneable and scalable neural network of 350+ parallel nodes based on a large area, multimode semiconductor laser. We implement online learning strategies based on reinforcement learning. Our system achieves high performance and a high classification bandwidth of 15KHz for the MNIST dataset. Our approach is highly scalable both in terms of classification bandwidth and neural network size.

A high-performance photonic reservoir, which utilizes the injection locking effect in a

highly multimodal semiconductor laser, has been developed. This innovative design allows for fully parallel and high-bandwidth operation. The output of this system is projected in space and imaged onto a digital micromirror device, which provides a readout and facilitates the hardware integration of programmable output weights. By using a highly multimodal semiconductor laser, the injection locking effect enables a large number of modes to be simultaneously locked to the injected signal, resulting in high dimensionality of the reservoir, reducing the computational time and complexity. The use of a digital micromirror device provides a flexible readout, allowing the output to be programmed to suit a range of applications. The hardware integration of programmable output weights enables the system to be optimized for specific tasks, improving performance and reducing power consumption.

In this paper, we experimentally demonstrated a novel all-optical reservoir computer with an all optical input mask. The combination of the binary random masks and the time-stretched ultrashort pulses has increased the system's classification performance. Compared with the traditional digital masks, this method shows superior classification performance in spoken-digit classification tasks and eliminates the need for high-speed modulation for digital masks.