Neuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics for machine learning algorithms, such as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on on-waveguide metasurfaces made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge2Sb2Te5 during phase transition to control the waveguide spatial modes with a very high precision of up to 64 levels in modal contrast. This contrast is used to represent the matrix elements, with 6-bit resolution and both positive and negative values, to perform MVM computation in neural network algorithms. We demonstrate a prototypical optical convolutional neural network that can perform image processing and recognition tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is promising toward large-scale photonic neural networks with ultrahigh computation throughputs.

Image sensors with internal computing capability enable in-sensor computing that can significantly reduce the communication latency and power consumption for machine vision in distributed systems and robotics. Two-dimensional semiconductors have many advantages in realizing such intelligent vision sensors because of their tunable electrical and optical properties and amenability for heterogeneous integration. Here, we report a multifunctional infrared image sensor based on an array of black phosphorous programmable phototransistors (bP-PPT). By controlling the stored charges in the gate dielectric layers electrically and optically, the bP-PPT’s electrical conductance and photoresponsivity can be locally or remotely programmed with 5-bit precision to implement an in-sensor convolutional neural network (CNN). The sensor array can receive optical images transmitted over a broad spectral range in the infrared and perform inference computation to process and recognize the images with 92% accuracy. The demonstrated bP image sensor array can be scaled up to build a more complex vision-sensory neural network, which will find many promising applications for distributed and remote multispectral sensing.

Light-emitting diodes (LEDs) based on III–V/II–VI materials have delivered a compelling performance in the mid-infrared (mid-IR) region, which enabled wide-ranging applications in sensing, including environmental monitoring, defense, and medical diagnostics. Continued efforts are underway to realize on-chip sensors via heterogeneous integration of mid-IR emitters on a silicon photonic chip, but the uptake of such an approach is limited by the high costs and interfacial strains, associated with the processes of heterogeneous integrations. Here, the black phosphorus (BP)-based van der Waals (vdW) heterostructures are exploited as room-temperature LEDs. The demonstrated devices emit linearly polarized light, and the spectra cover the technologically important mid-IR atmospheric window. Additionally, the BP LEDs exhibit fast modulation speed and exceptional operation stability. The measured peak extrinsic quantum efficiency is comparable to the III–V/II–VI mid-IR LEDs. By leveraging the integrability of vdW heterostructures, we further demonstrate a silicon photonic waveguide-integrated BP LED.

Herein we introduce af acile,s olution-phase protocol to modify the Lewis basic surface of few-layer black phosphorus (bP) and demonstrate its effectiveness at providing ambient stability and tuning of electronic properties.Commercially available group 13 Lewis acids that range in electrophilicity,steric bulk, and Pearson hard/soft-ness are evaluated. The nature of the interaction between the Lewis acids and the bP lattice is investigated using arange of microscopic (optical, atomic force,s canning electron) and spectroscopic (energy dispersive,X-ray photoelectron) methods.A la nd Ga halides are most effective at preventing ambient degradation of bP (> 84 hf or AlBr 3), and the resulting field-effect transistors show excellent IV characteristics,p hotocurrent, and current stability,a nd are significantly p-doped. This protocol, chemically matched to bP and compatible with device fabrication, opens ap ath for deterministic and persistent tuning of the electronic properties in bP.