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(Peer-Reviewed) Programmable directional photonic spiking neuron based on a non-Hermitian silicon microresonator
Stefano Biasi, Bülent Aslan, Stefano Gretter, Davide Olivieri, Alessandro Foradori, Riccardo Franchi, Lorenzo Pavesi
Nanoscience Laboratory, Department of Physics, University of Trento, Trento 38123, Italy
Opto-Electronic Science , 2026-08-26
Abstract

Traditional computing architectures are increasingly constrained by the “Von Neumann bottleneck”, motivating energy-efficient, brain-inspired neuromorphic systems. Silicon photonic microresonators can emulate key neuronal dynamics such as spiking and excitability. However, in reciprocal microresonators, forward spike emission and backward signals are intrinsically linked, limiting independent control of directionality, feedback and inter-node back-action.

Here, we overcome this limitation by exploiting engineered intermodal coupling in non-Hermitian photonic resonators to realize directional excitability in all-optical neurons. First, we show that Taiji microresonators exhibit direction-dependent self-pulsing thresholds, enabling a single device to act as a spiking neuron in one direction while remaining quiescent in the opposite. Then, we introduce the dynamically reconfigurable unified microresonator (DRUM), which enables electrical control of intermodal coupling amplitude and phase. This allows independent control of forward spike emission and backward signals, suppression or enhancement of back-propagation, and active tuning of neuronal properties. We experimentally demonstrate directional spiking asymmetry alongside tunability of temporal integration and refractory period, establishing DRUM as a programmable photonic spiking neuron.

Finally, network-level simulations show that controlled back-action can mediate synaptic-like behavior, including inhibition and synchronization. These results identify reconfigurable non-Hermitian silicon microresonators as promising building blocks for photonic spiking nodes with programmable directionality, back-action, and temporal response.
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  • Physics-informed neural fields enable blind aberration correction for partially coherent quantitative phase imaging        Mutual empowerment of artificial intelligence and metasurfaces: intelligent nanophotonics and optical intelligence
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