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Abstract Optical phase modulators are critical components in integrated photonics, but conventional designs suffer from a trade-off between modulation efficiency and optical loss. Two-dimensional materials like graphene offer strong electro-optic effects, yet their high optical absorption at telecom wavelengths leads to significant insertion losses. Although monolayer transition metal dichalcogenides (TMDs) provide exceptional telecom-band transparency for low-loss electro-refractive response, their practical implementation in phase modulators requires top electrodes to enable vertical electric field tuning, which typically introduces parasitic absorption. Here, we address this challenge by developing hybrid tungsten oxyselenide/graphene (TOS/Gr) electrodes that minimize optical loss while enabling efficient phase modulation in TMD-based devices. The UV-ozone-converted TOS (from WSe₂) acts as a heavy p-type dopant for graphene, making the graphene transparent in the NIR region while enhancing its conductivity. Our complete device integrates a hybrid TOS/graphene transparent electrode with a hexagonal boron nitride dielectric spacer and monolayer WS₂electro-optic material on a SiN microring platform. This achieves a high modulation efficiency of 0.202 V·cm while maintaining an exceptionally low extinction ratio change of just 0.08 dB, demonstrating superior performance compared to modulators employing conventional electrodes. Our breakthrough in near-lossless phase modulation opens new possibilities for energy-efficient optical communications, photonic computing, and fault-tolerant quantum networks. 

Abstract Conventional imaging and recognition systems require an extensive amount of data storage, pre-processing, and chip-to-chip communications as well as aberration-proof light focusing with multiple lenses for recognizing an object from massive optical inputs. This is because separate chips ( i . e ., flat image sensor array, memory device, and CPU) in conjunction with complicated optics should capture, store, and process massive image information independently. In contrast, human vision employs a highly efficient imaging and recognition process. Here, inspired by the human visual recognition system, we present a novel imaging device for efficient image acquisition and data pre-processing by conferring the neuromorphic data processing function on a curved image sensor array. The curved neuromorphic image sensor array is based on a heterostructure of MoS 2 and poly(1,3,5-trimethyl-1,3,5-trivinyl cyclotrisiloxane). The curved neuromorphic image sensor array features photon-triggered synaptic plasticity owing to its quasi-linear time-dependent photocurrent generation and prolonged photocurrent decay, originated from charge trapping in the MoS 2 -organic vertical stack. The curved neuromorphic image sensor array integrated with a plano-convex lens derives a pre-processed image from a set of noisy optical inputs without redundant data storage, processing, and communications as well as without complex optics. The proposed imaging device can substantially improve efficiency of the image acquisition and recognition process, a step forward to the next generation machine vision.