The Track on Materials and Devices for Ultra‑Low‑Power Neuromorphic Systems invites original research contributions that explore emerging materials, novel device concepts, and energy‑efficient physical mechanisms enabling the next generation of neuromorphic computing. As the demand for real‑time, always‑on, and biologically inspired intelligence grows, breakthroughs at the materials and device level are essential to achieving ultra‑low‑power, high‑density, and scalable neuromorphic hardware. This track brings together researchers from materials science, nanoelectronics, device physics, neuromorphic engineering, and computer architecture to advance the foundations of future brain‑inspired systems.
We welcome submissions including, but not limited to, the following areas:
1. Emerging Materials for Neuromorphic Devices
- Novel materials for synaptic and neuronal behavior (oxide‑based, chalcogenide, ferroelectric, 2D materials, organic materials)
- Phase‑change, resistive switching, ferroelectric, and spintronic materials for neuromorphic computing
- Material engineering for improved endurance, retention, and variability control
- Bio‑inspired and biomimetic materials for adaptive and plastic behavior
2. Ultra‑Low‑Power Neuromorphic Devices
- Memristive, ferroelectric, spintronic, and photonic neuromorphic devices
- Subthreshold and near‑threshold CMOS devices for spiking computation
- Devices supporting local learning rules (STDP, Hebbian, homeostatic plasticity)
- Multi‑state and analog synaptic devices for high‑density learning systems
- Device concepts exploiting stochasticity, noise, or physical dynamics
3. Device–Circuit Co‑Design
- Co‑design of emerging devices with analog, mixed‑signal, or digital neuromorphic circuits
- Crossbar arrays and in‑memory computing structures for SNNs
- Peripheral circuit innovations for ultra‑low‑power operation
- Variability‑aware design, compensation, and calibration techniques
4. 3D Integration and Heterogeneous Technologies
- 3D‑IC, monolithic 3D, and chiplet‑based integration of neuromorphic devices
- Vertical synaptic fabrics and high‑density interconnect technologies
- Thermal, reliability, and manufacturability considerations in stacked neuromorphic systems
5. Characterization, Modeling, and Benchmarking
- Compact models for emerging neuromorphic devices
- Experimental characterization of synaptic and neuronal behavior
- Endurance, retention, noise, and variability analysis
- Benchmarking of device‑level energy, latency, and scalability
6. Applications and Demonstrations
- Device‑level demonstrations of learning, adaptation, and spiking behavior
- Ultra‑low‑power edge intelligence and always‑on sensing
- Event‑driven perception, robotics, and biomedical applications
- Comparative studies of emerging devices vs. CMOS baselines
