Novel Devices for Neuromorphic Computing — Memristors, PCM, RRAM

← Back to the NeuroCore 2026 Page

Track Chair

---

Xumeng Zhang
Fudan University

Neuromorphic computing is entering a transformative era driven by breakthroughs in emerging memory and device technologies. This track invites high‑quality contributions that explore novel devices, materials, architectures, and integration strategies enabling next‑generation neuromorphic systems. As the field moves beyond conventional CMOS, devices such as memristors, phase‑change memory (PCM), and resistive RAM (RRAM) are redefining what is possible in energy‑efficient, massively parallel, and brain‑inspired computation.
We welcome original research, visionary concepts, and experimental demonstrations that advance the state of the art in device‑level innovation and its system‑level implications.

Topics of Interest

Submissions may include, but are not limited to:

1. Emerging Device Technologies

• Novel memristive devices and material systems
• Advances in PCM, RRAM, FeFETs, and other non‑volatile memory technologies
• Device physics, modeling, and characterization for neuromorphic workloads
• Reliability, endurance, variability, and noise analysis

2. Neuromorphic Circuits and Architectures

• Crossbar arrays and in‑memory computing primitives
• Analog and mixed‑signal neuromorphic circuits
• Device‑circuit co‑design for synaptic and neuronal functions
• 3D integration, heterogeneous stacking, and system‑level scaling

3. Algorithms and Applications Enabled by Novel Devices

• Learning algorithms tailored to memristive/PCM/RRAM characteristics
• On‑device learning, STDP, and biologically inspired adaptation
• Applications in edge AI, robotics, sensing, and ultra‑low‑power inference
• Benchmarks and performance evaluation frameworks

4. Fabrication, Integration, and Manufacturing

• CMOS‑compatible processes and integration challenges
• Variability mitigation and yield‑aware design
• Emerging materials and fabrication techniques for neuromorphic devices

5. Cross‑Disciplinary and Visionary Contributions

• Device‑to‑algorithm co‑optimization
• New paradigms for analog computing and hybrid digital‑analog systems
• Roadmaps, challenges, and future directions for neuromorphic hardware

PATRON, HOST, and SPONSORS