Scalable native signed optical computing enabled by dual-wavelength incoherent multiplexing

TL;DR

This paper introduces a dual-wavelength incoherent photonic architecture on lithium niobate that natively supports signed operations, enabling scalable and hardware-efficient optical neural networks.

Contribution

The authors demonstrate a novel dual-wavelength scheme that supports native signed inputs and weights, reducing hardware overhead for large-scale optical neural network implementations.

Findings

Device bandwidth exceeds 40 GHz.

Achieved 4-quadrant optical multiplication with 1.27% error.

Neural network classification accuracy of 95.1% on Moons dataset.

Abstract

Incoherent photonic neural networks (PNNs) provide a robust platform for analog optical computing, yet efficient implementation of native signed operations remains challenging. Existing incoherent PNNs approaches often require additional spatial channels or temporal encoding steps to represent bipolar input signals, resulting in hardware overhead that scales with system size. Here, we demonstrate a dual-wavelength incoherent photonic architecture that natively supports both signed inputs and signed weights on a thin-film lithium niobate platform. By encoding complementary signal components onto two wavelength channels and performing computation within a shared physical path, the proposed scheme eliminates duplicated weighting units. As a result, the additional hardware overhead associated with signed computation remains constant per multiply accumulate operation, independent of matrix size. The fabricated device exhibits a modulation bandwidth exceeding 40 GHz and achieves four-quadrant optical multiplication with a standard deviation error of 1.27%. System-level functionality is validated through neural-network classification, achieving 95.1% accuracy on the Moons dataset and 91.63% on MNIST. These results establish a practical route toward scalable incoherent photonic computing systems with native bipolar processing capability.