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DOE OSTI2025

Nanoscale Tracking of the High-Temperature Spin-State Transition in LaCoO 3

The high-temperature spin and electronic transitions in LaCoO 3 have recently been leveraged to create neuromorphic (brain-inspired) devices. While these devices have shown the potential for impactful functionality in next-generation computing systems, the nanoscale dynamics of the spin and electronic transitions that underlie their operation are not well understood. Inhomogeneities related to interfaces, electrode contacts, strain, and crystal defects can all affect device performance, making nanoscale characterization of the transitions essential for producing consistent and reliable devices. Here, we demonstrate the first nanoscale in situ measurement of the spin transition in LaCoO 3 at device-relevant temperatures (25–325 °C) over length scales of tens of nanometers using STEM-EELS. This measurement is enabled by an Al 2 O 3 coating, which prevents unwanted reduction of the LaCoO 3 specimen at high temperature and vacuum. The detailed understanding of LaCoO 3 transition dynamics enabled by such measurements will be crucial for optimizing LaCoO 3 -based neuromorphic devices and increasing reliability for real-world application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Neuromorphic scaling advantages for energy-efficient random walk computations

Neuromorphic computing, which aims to replicate the computational structure and architecture of the brain in synthetic hardware, has typically focused on artificial intelligence applications. What is less explored is whether such brain-inspired hardware can provide value beyond cognitive tasks. Here we show that the high degree of parallelism and configurability of spiking neuromorphic architectures makes them well suited to implement random walks via discrete-time Markov chains. Overall, these random walks are useful in Monte Carlo methods, which represent a fundamental computational tool for solving a wide range of numerical computing tasks. Using IBM’s TrueNorth and Intel’s Loihi neuromorphic computing platforms, we show that our neuromorphic computing algorithm for generating random walk approximations of diffusion offers advantages in energy-efficient computation compared with conventional approaches. We also show that our neuromorphic computing algorithm can be extended to more sophisticated jump-diffusion processes that are useful in a range of applications, including financial economics, particle physics and machine learning.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Operando microscopy for neuromorphic hardware

Microscopy techniques can uncover the physical properties and dynamic behaviours of materials, driving the discovery of emergent phenomena and guiding the design of next-generation computing hardware. As artificial intelligence becomes pervasive, the demand for high-performance materials to support sustainable information technologies is growing. Here, this Review highlights state-of-the-art imaging from electron and X-ray to optical techniques to probe the dynamics of neuromorphic materials, including operando characterization of devices. We examine design principles for neuromorphic materials, along with obstacles that hinder their development. Emphasis is placed on spatially and temporally resolved approaches that capture state changes including phase transitions, ferroic switching and spin-wave propagation that emulate biological components such as neurons, synapses and their connectivity. We discuss challenges in operando characterization and the integration of artificial intelligence-driven analysis for feedback-guided material discovery. Finally, we outline opportunities for real-time imaging of neuromorphic systems, paving the way towards adaptive, brain-inspired hardware.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗