Stochastic transition in synchronized spiking nanooscillators.

This work reports that synchronization of Mott material-based nanoscale coupled spiking oscillators can be drastically different from that in conventional harmonic oscillators. We investigated the synchronization of spiking nanooscillators mediated by thermal interactions due to the close physical proximity of the devices. Controlling the driving voltage enables in-phase 1:1 and 2:1 integer synchronization modes between neighboring oscillators. Transition between these two integer modes occurs through an unusual stochastic synchronization regime instead of the loss of spiking coherence. In the stochastic synchronization regime, random length spiking sequences belonging to the 1:1 and 2:1 integer modes are intermixed. The occurrence of this stochasticity is an important factor that must be taken into account in the design of large-scale spiking networks for hardware-level implementation of novel computational paradigms such as neuromorphic and stochastic computing.

Reconfigurable Cascaded Thermal Neuristors for Neuromorphic Computing.

While the complementary metal-oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, an alternative route is explored based on a new class of spiking oscillators called "thermal neuristors", which operate and interact solely via thermal processes. Utilizing the insulator-to-metal transition (IMT) in vanadium dioxide, a wide variety of reconfigurable electrical dynamics mirroring biological neurons is demonstrated. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. This study establishes the foundation for scalable and energy-efficient thermal neural networks, fostering progress in brain-inspired computing.

Defect-Mediated Faceted Lithium Nucleation on Carbon Composite Substrates.

The formation of uniform, nondendritic seeds is essential to realizing dense lithium (Li) metal anodes and long-life batteries. Here, we discover that faceted Li seeds with a hexagonal shape can be uniformly grown on carbon-polymer composite films. Our investigation reveals the critical role of carbon defects in serving as the nucleation sites for their formation. Tuning the density and spatial distribution of defects enables the optimization of conditions for faceted seed growth. Raman spectral results confirm that lithium nucleation indeed starts at the defect sites. The uniformly distributed crystalline seeds facilitate low-porosity Li deposition, effectively reducing Li pulverization during cycling and unlocking the fast-charging ability of Li metal batteries. At a 1 C rate, full cells using LiNi0.8Mn0.1Co0.1O2 cathode (4.5 mA h cm-2) paired with a lithium anode grown on carbon composite films achieve a 313% improvement in cycle life compared to baseline cells. Polymer composites with carbonaceous materials rich in defects are scalable, low-cost substrates for high-rate, high-energy-density batteries.

Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge.

CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.