image: Figure 1 | Multi-task, large-scale integrated optical vision processor using ultrafast parallel nanofabrication. a, Flowchart of the multi-task integrated ONN processor. b, Illustration of the randomized multi-focus TPL system printing at a rate of 0.267 million neurons per minute for ONN device fabrication. To achieve clearly isolated diffractive pixels with both high spatial resolution and fabrication throughput, we propose a random scanning strategy. In this method, each 10 μm × 10 μm region is patterned with pixels composed of four adjacent voxels arranged in a quadrilateral configuration, spaced 200 nm apart, and exposed simultaneously in a single-shot process. c, Design of the 3D-printed diffractive layer. Each unit is fabricated from photoresist (n =1.520 at 520 nm) on a soda lime glass substrate (n = 1.527 at 520 nm). Five phase gradients with π/5 increments are implemented, facilitated by a height-modulated, untrained design featuring a random and uniform distribution with five distinct levels (heights of 100-500 nm). d, Comparison with previously reported works including both 3D printing, photolithography and EBL (such as neuron number, density and fabrication speed). e, Ultra-low-cost nanoimprinting for mass production. The reusable mold is generated by PDMS soft lithography, and large-scale replication is achieved via UV nanoimprinting with high throughput and high feature fidelity.
Credit: Chaoran Huang et al.
The rapid advancement of deep learning has revolutionized machine vision, yet traditional von Neumann hardware struggles with the speed and power demands of processing high-resolution data. Optical neural networks (ONNs) provide a promising alternative by performing computations at the speed of light with minimal energy. However, implementing ONNs at visible wavelengths requires millions of wavelength-scale neurons, presenting immense fabrication challenges for traditional methods.
In a new paper published in Light: Advanced Manufacturing, a team of scientists, led by Professor Shih-Chi Chen and Professor Chaoran Huang from The Chinese University of Hong Kong, introduced a breakthrough high-throughput randomized multi-focus two-photon lithography (TPL) platform. By employing a novel parallel scanning strategy via holographic light-field control, the team successfully fabricated four million 500-nm neurons on a millimeter-scale chip in just 15 minutes.
This work utilizes a task-agnostic optical encoder that performs random projections through a 3D-printed diffractive layer. The system integrates this optical device and a compact camera with a simple digital neural network readout layer parameterized by as few as 1,000 weights. Experimental results demonstrate superior performance with 97%—99% classification accuracy in diverse tasks, including hand-drawn figure recognition, human action recognition, and human face keypoint detection.
Unlike conventional lithography, which is time-consuming and costly, this multi-focus TPL approach supports rapid prototyping and is compatible with ultra-low-cost UV nanoimprinting for mass production. This capability bridges the gap between high-precision prototyping and scalable manufacturing, paving the way for the broad deployment of integrated optical vision processors in applications such as LiDAR, biomedical diagnostics, and human-computer interaction.
These scientists highlight the versatility of their technique: “Since the diffractive layer implements an untrained random projection, it serves as a task-agnostic optical encoder, while task adaptation is achieved exclusively through retraining of the lightweight digital readout layer.”
Regarding scalability, the scientists noted: “Unlike conventional metasurfaces or other optical neural network platforms, which typically rely on one-off or costly fabrication schemes and thus face significant barriers to scalability, our method intrinsically supports cost-effective mass production. This unique capability bridges the gap between high-precision prototyping and scalable device manufacturing, thereby offering a practical and economical pathway towards the deployment of optical neural networks.”
Looking ahead, the team forecasts: “With broader material selection and nanoimprint replication strategies, the operational range could potentially extend from the near-UV to infrared regimes. In addition, centimeter-scale devices are feasible through tiled writing and imprint replication, enabling larger optical apertures for practical imaging systems.”
Journal
Light: Advanced Manufacturing
Article Title
Multi-task large-scale integrated optical vision processor using ultra-fast parallel nanofabrication