journal article·2026-05-01Open access

Reservoir computing using a Si-integrated electro-optic oxide: A numerical study

Xiaoru Zhang, Alexander A. Demkov

AIP Advances· Volume 16· Issue 5

Abstract

In this numerical study, we investigate the performance of an electro-optic reservoir under the practical constraints of a Si-integrated photonic design enabled by barium titanate, an emergent electro-optic material. Reservoir computing is a compelling neuromorphic architecture due to its natural compatibility with many physical systems. Among available implementations, the electro-optic reservoir, where an optical delay feedback loop serves as the reservoir, offers energy-efficient computation without the need for a separate memory, making its chip-scale implementation very appealing. Barium titanate, with its exceptionally large Pockels' coefficient, has recently enabled significant advances in compact electro-optic modulators, positioning it as a suitable platform for such a chip-scale electro-optic reservoir. We benchmark the performance of the electro-optic reservoir over various tasks and introduce a method of constructing reservoir states that results in significant performance improvement. Furthermore, we demonstrate the superior energy efficiency of barium–titanate-based devices, owing to their very efficient electro-optic modulation. Our results show that compact, energy-efficient electro-optic reservoirs are feasible using barium–titanate-based integrated Si photonic devices.
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Research context

OpenAlex topics: Neural Networks and Reservoir Computing · Magneto-Optical Properties and Applications · Advanced Memory and Neural Computing

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Physics, The University of Texas , Ausitn, Texas 78712,

Funding

Multidisciplinary University Research Initiative from the Air Force Office of Scientific Research

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