Optoelectronic sensors with photonic memory for trajectory prediction
Fedor Ivanov, Victor V. Krasnikov, Andrey A. Grunin, Artem V. Chetvertukhin, Andrey A. Fedyanin · DOAJ (DOAJ: Directory of Open Access Journals) · 2026
Neuromorphic vision systems, inspired by biological vision, offer high energy efficiency and sensor-level data processing. Among these systems, neuromorphic optoelectronic sensors are particularly promising because they exhibit biomimetic responses to incident light. A key feature of such devices is an inherent memory-like response, hereafter referred to as “photonic memory”: sensor conductance depends not only on instantaneous illumination but also on exposure history, enabling temporal integration of information. Here, we study how photonic memory impacts trajectory prediction by comparing a ZnO-based photonic-memory sensor with a conventional image sensor in an experimentally grounded numerical framework. We experimentally characterize the non-volatile conductivity dynamics of the synapse under controlled illumination and darkness and develop a compact mathematical model of its memory behavior. The resulting imaging model is further validated using experimentally reconstructed motion patterns based on measured ZnO-based synaptic-pixel responses. We then evaluate both sensors in a trajectory prediction task across different noise levels and numbers of input frames supplied to an artificial neural network. Our results show that, within this experimentally grounded numerical benchmark, photonic memory provides a clear advantage in the single-frame regime, where the conventional sensor can rely only on spatial priors rather than explicit temporal information. However, as the number of input frames increases, this advantage diminishes and is eventually lost. A conventional image sensor then achieves superior multi-frame performance. These findings suggest that photonic memory alone is insufficient for optimal multi-frame trajectory prediction and must be complemented by additional biologically inspired mechanisms, such as synaptic-like depression implemented at the level of device physics and sensor architecture. More broadly, our work outlines design directions for optoelectronic vision hardware that exploit a richer and more versatile space of device-level temporal responses.