Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip

Optical machine learning has emerged as an important research area that, by leveraging the advantages inherent to optical signals, such as parallelism and high speed, paves the way for a future where optical hardware can process data at the speed of light. In this work, we present such optical devices for data processing in the form of single-layer nanoscale holographic perceptrons trained to perform optical inference tasks. We experimentally show the functionality of these passive optical devices in the example of decryptors trained to perform optical inference of single or whole classes of keys through symmetric and asymmetric decryption. The decryptors, designed for operation in the near-infrared region, are nanoprinted on complementary metal-oxide-semiconductor chips by galvo-dithered two-photon nanolithography with axial nanostepping of 10 nm(1,2), achieving a neuron density of >500 million neurons per square centimetre. This power-efficient commixture of machine learning and on-chip integration may have a transformative impact on optical decryption(3), sensing(4), medical diagnostics(5) and computing(6,7).

Automated summary

Optical machine learning utilizes the advantages of optical signals to process data at the speed of light. In this study, optical devices in the form of single-layer nanoscale holographic perceptrons were presented for optical inference tasks. The combination of machine learning and on-chip integration in these devices shows promise for transformative impacts in optical decryption, sensing, medical diagnostics, and computing.