Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart.

A magnetic digital microfluidic platform manipulates droplets on an open surface.

Biological sample processing involves purifying target analytes from various sample matrices and concentrating them to a small volume from a large volume of crude sample. This complex process is the major obstacle for developing a microfluidic diagnostic platform. In this study, we present a microfluidic device that can continuously separate and concentrate pathogenic bacterial cells from complex sample matrices such as cerebrospinal fluid and whole blood. Having overcome critical limitations of dielectrophoretic (DEP) operation in physiological media of high conductivity, we utilized target specific DEP techniques to incorporate cell separation, medium exchange, and target concentration into an integrated platform. The proposed microfluidic device can uptake mL volumes of crude biological sample and selectively concentrate target cells into a submicrolitre volume, providing ∼10⁴ fold of concentration. We designed the device based on the electrokinetic theory and electric field simulation, and tested the device performance with different sample types. The separation efficiency of the device was as high as 97.0% for a bead mixture in TAE buffer and 94.3% and 87.2% for E. coli in human cerebrospinal fluid and blood, respectively. A capture efficiency of 100% was achieved in the concentration chamber. With a relatively simple configuration, the proposed device provides a robust method of continuous sample processing, which can be readily integrated into a fully automated microfluidic diagnostic platform for pathogen detection and quantification.

Particle trapping and binding in optical potential wells provide a versatile platform for various biomedical applications. However, implementation systems to study multi-particle contact interactions in an optical lattice remain rare. By configuring an optofluidic lattice, we demonstrate the precise control of particle interactions and functions such as controlling aggregation and multi-hopping. The mean residence time of a single particle is found considerably reduced from 7 s, as predicted by Kramer’s theory, to 0.6 s, owing to the mechanical interactions among aggregated particles. The optofluidic lattice also enables single-bacteria-level screening of biological binding agents such as antibodies through particle-enabled bacteria hopping. The binding efficiency of antibodies could be determined directly, selectively, quantitatively and efficiently. This work enriches the fundamental mechanisms of particle kinetics and offers new possibilities for probing and utilising unprecedented biomolecule interactions at single-bacteria level.