MOF-based memristors for in-sensor computing: Materials, devices, and integration
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ABSTRACT
Based on the in-sensor computing strategy, integrating sensing and computing in memristors offers a promising solution to the limitations (excessive power consumption, high latency, poor compatibility) of traditional "sensing-computing separation" sensory recognition systems. However, mainstream memristive materials like metal oxides lack reliable multimodal sensing and information fusion capabilities, hindering their effectiveness. Honored by the 2025 Nobel Prize in Chemistry for their customizable structure and function, metal-organic frameworks (MOFs) uniquely address this by unifying multimodal sensing and on-site memristive computing. This perspective adopts a "materials-device-integration" framework to elaborate MOFs’ foundational role, reveal cross-scale mechanism bottlenecks (e.g., material instability, device crosstalk, integration incompatibility) and targeted solutions (e.g., ligand engineering, frequency-dependent operation, technology optimization), and outline future directions (e.g., bio-integrated systems, adaptive edge networks, AI-driven MOF design). It highlights MOFs’ potential to overcome traditional limitations and advance "material-centric computing" for next-generation intelligent systems. -
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Qian L., Xue S. and Ding G. (2026). MOF-based memristors for in-sensor computing: Materials, devices, and integration. The Innovation Materials 4:100240. https://doi.org/10.59717/j.xinn-mater.2026.100240
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Qian L., Xue S. and Ding G. (2026). MOF-based memristors for in-sensor computing: Materials, devices, and integration. The Innovation Materials 4:100240. https://doi.org/10.59717/j.xinn-mater.2026.100240
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Figure 1.
Illustration of the advantages, challenges, and strategies of MOFs for applications in in-sensor computing systems from the perspectives of materials, devices, and integrations.
