Advanced all-optical classification using orbital-angular-momentum-encoded diffractive networks

Abstract

As a successful case of combining deep learning with photonics, the research on optical machine learning has recently undergone rapid development. Among various optical classification frameworks, diffractive networks have been shown to have unique advantages in all-optical reasoning. As an important property of light, the orbital angular momentum (OAM) of light shows orthogonality and mode-infinity, which can enhance the ability of parallel classification in information processing. However, there have been few all-optical diffractive networks under the OAM mode encoding. Here, we report a strategy of OAM-encoded diffractive deep neural network (OAM-encoded D 2 NN) that encodes the spatial information of objects into the OAM spectrum of the diffracted light to perform all-optical object classification. We demonstrated three different OAM-encoded D 2 NNs to realize (1) single detector OAM-encoded D 2 NN for single task classification, (2) single detector OAM-encoded D 2 NN for multitask classification, and (3) multidetector OAM-encoded D 2 NN for repeatable multitask classification. We provide a feasible way to improve the performance of all-optical object classification and open up promising research directions for D 2 NN by proposing OAMencoded D 2 NN.

Generation of dynamic rotation propagation vortex beam by a Fibonacci series annular subzone vortex phase

“…We also define the focal length of the proposed FVB. Because of the novel property of FVB, we expect that FVB will be the novel OV and have the potential applications in optical vortex information encryption [46], optical communications, nanofabrication [47,48] and optical tweezers [49].…”

OAM-based diffractive all-optical classification

“…In their recently published paper, Zhang et al bring the power of OAM mode multiplexing to all-optical object classification using diffractive optical networks 10 . In their framework, information about the input object class is carried by the OAM distribution of the light.…”

“…1(c). For example, the reported blind testing accuracy for M handwritten digits on the input plane with M detectors is 70.94% for M=2 and 40.13% for M=4 10 …”

“…8 A helical wavefront expðilφÞ, where φ is the azimuthal angle and l is the helical mode index, carries a form of angular momentum known as the orbital angular momentum (OAM), which bears the potential to multiplex large amounts of optical information within the same physical space due to the theoretically unbounded mode index l. 9 In their recently published paper, Zhang et al bring the power of OAM mode multiplexing to all-optical object classification using diffractive optical networks. 10 In their framework, information about the input object class is carried by the OAM distribution of the light. The beam that illuminates the object comprises several OAM mode components l, one corresponding to each object class, as shown in Fig.…”

“…For example, the reported blind testing accuracy for M handwritten digits on the input plane with M detectors is 70.94% for M ¼ 2 and 40.13% for M ¼ 4. 10 Zhang et al also showed that OAM-encoded D 2 NNs are resilient to misalignment errors, which is a major advantage for practical settings. By multiplexing a multitude of OAM modes on the input beam, it is possible to utilize OAM-encoded D 2 NNs for distinguishing among a large number of classes of the input object.…”