## 1 Introduction
Conventional electronic memristors are confined to handling electrical signals, limiting their utility to electronic computing and storage functions, without the ability to interact with light-based information. In stark contrast, optoelectronic memristors (OEMs) incorporate light-responsive elements, granting them the unique capacity to directly sense and process optical signals in addition to their inherent memory and computing capabilities [16]. This distinctive feature positions OEMs as more versatile devices, particularly in applications like neuromorphic computing, where they can adeptly execute tasks such as image recognition and sensing. Consequently, OEM devices exhibit the capability to interact with and respond to the environment in a manner akin to the information-processing mechanisms of biological systems. An illustrative example of this versatility is presented by [PERSON] et al., who showcased the neuromorphic computing prowess of optoelectronic memristors in in-sensor computing applications. In a notable demonstration, these devices exhibited their effectiveness in face recognition tasks, achieving an impressive accuracy rate of 86.7% [17]. This underscores the potential of OEMs to revolutionize not only traditional electronic computing and storage, but also to extend their impact into domains where direct interaction with optical signals is crucial for enhanced functionality and performance. Optical memristors, in comparison to their electronic and optoelectronic counterparts, offer distinctive advantages characterized by high bandwidth, energy efficiency, and rapid processing speeds. Operating by modulating light intensity while maintaining a nonvolatile state, these optical memristors prove advantageous for applications in high-bandwidth scenarios, including neuromorphic computing, machine learning, and artificial intelligence (AI). The recent progress in on-chip integrated optical memristors has unlocked new potentials for reprogrammable photonic integrated circuits (PICs), addressing challenges in achieving nonvolatility that were previously encountered. This development capitalizes on the persistent response characteristics of optical memristors, paving the way for novel applications [18]. In a comprehensive review, [PERSON] and collaborators explored advancements in optically readable memories, aiming to enhance storage and computational capabilities. By leveraging photonic integrated circuits (PICs) and optical nanomaterials, this approach seeks to overcome the von Neumann bottleneck. While photonic memories have not yet matched the storage densities of their electronic counterparts, their inherent analog nature and exceptional bandwidth position them favorably for innovative computing strategies. This application addresses the limitations inherent in conventional electronic-based synapses within neuromorphic computing, mitigating the trade-off between bandwidth and connection density. Photonic synapses, characterized by ultrahigh propagation speed, high bandwidth, and low crosstalk, contribute to enhanced computing speed [19; 20]. Notably, the optical memristive behavior has been observed in ReRAM devices incorporating integrated plasmonic waveguides for optical readout functionality [21]. Furthermore, [PERSON] and colleagues recently demonstrated the first experimental quantum-optical memristor, operating on single-photon states. This quantum-optical memristor, based on integrated photonics, exhibits memristive dynamics with potential applications in advancing quantum neuromorphic architectures [22]. Therefore, by harnessing the unique properties of light, optical memristors facilitate faster data transmission and processing with lower energy consumption, positioning them as highly suitable for next-generation computing technologies [18]. However, despite their crucial role in high-speed optical communication systems, quantum computing, and precision optical sensing, photonic/optical memristors encounter limitations in temperature-related applications, such as thermal-energy-management systems.