{"id":11493,"date":"2026-02-12T11:11:37","date_gmt":"2026-02-12T11:11:37","guid":{"rendered":"https:\/\/mcsoc-forum.org\/site\/?page_id=11493"},"modified":"2026-02-21T06:22:13","modified_gmt":"2026-02-21T06:22:13","slug":"neurocore-t15","status":"publish","type":"page","link":"https:\/\/mcsoc-forum.org\/site\/index.php\/neurocore-t15\/","title":{"rendered":"Novel Devices for Neuromorphic Computing \u2014 Memristors, PCM, RRAM"},"content":{"rendered":"\n
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\n \n \u2190 Back to the NeuroCore 2026 Page\n <\/a>\n<\/p>\n

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Track Chair<\/h2>\n
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\"Xumeng<\/figure>

Xumeng Zhang<\/a><\/strong>
Fudan University<\/span><\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

Neuromorphic computing is entering a transformative era driven by breakthroughs in emerging memory and device technologies. This track invites high\u2011quality contributions that explore novel devices, materials, architectures, and integration strategies<\/strong> enabling next\u2011generation neuromorphic systems. As the field moves beyond conventional CMOS, devices such as memristors, phase\u2011change memory (PCM), and resistive RAM (RRAM)<\/strong> are redefining what is possible in energy\u2011efficient, massively parallel, and brain\u2011inspired computation.
We welcome original research, visionary concepts, and experimental demonstrations that advance the state of the art in device\u2011level innovation and its system\u2011level implications.<\/p>\n\n\n\n

Topics of Interest<\/strong><\/h2>\n\n\n\n

Submissions may include, but are not limited to:<\/p>\n\n\n\n

1. Emerging Device Technologies<\/strong><\/h3>\n\n\n\n
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  • Novel memristive devices and material systems<\/li>\n\n\n\n
  • Advances in PCM, RRAM, FeFETs, and other non\u2011volatile memory technologies<\/li>\n\n\n\n
  • Device physics, modeling, and characterization for neuromorphic workloads<\/li>\n\n\n\n
  • Reliability, endurance, variability, and noise analysis<\/li>\n<\/ul>\n\n\n\n

    2. Neuromorphic Circuits and Architectures<\/strong><\/h3>\n\n\n\n
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    • Crossbar arrays and in\u2011memory computing primitives<\/li>\n\n\n\n
    • Analog and mixed\u2011signal neuromorphic circuits<\/li>\n\n\n\n
    • Device\u2011circuit co\u2011design for synaptic and neuronal functions<\/li>\n\n\n\n
    • 3D integration, heterogeneous stacking, and system\u2011level scaling<\/li>\n<\/ul>\n\n\n\n

      3. Algorithms and Applications Enabled by Novel Devices<\/strong><\/h3>\n\n\n\n
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      • Learning algorithms tailored to memristive\/PCM\/RRAM characteristics<\/li>\n\n\n\n
      • On\u2011device learning, STDP, and biologically inspired adaptation<\/li>\n\n\n\n
      • Applications in edge AI, robotics, sensing, and ultra\u2011low\u2011power inference<\/li>\n\n\n\n
      • Benchmarks and performance evaluation frameworks<\/li>\n<\/ul>\n\n\n\n

        4. Fabrication, Integration, and Manufacturing<\/strong><\/h3>\n\n\n\n
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        • CMOS\u2011compatible processes and integration challenges<\/li>\n\n\n\n
        • Variability mitigation and yield\u2011aware design<\/li>\n\n\n\n
        • Emerging materials and fabrication techniques for neuromorphic devices<\/li>\n<\/ul>\n\n\n\n

          5. Cross\u2011Disciplinary and Visionary Contributions<\/strong><\/h3>\n\n\n\n
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          • Device\u2011to\u2011algorithm co\u2011optimization<\/li>\n\n\n\n
          • New paradigms for analog computing and hybrid digital\u2011analog systems<\/li>\n\n\n\n
          • Roadmaps, challenges, and future directions for neuromorphic hardware<\/li>\n<\/ul>\n\n\n\n