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Non‐von‐Neumann computing using neuromorphic systems based on two‐terminal resistive nonvolatile memory elements has emerged as a promising approach, but its full potential has not been realized due to the lack of materials and devices with the appropriate attributes. Unlike memristors, which require large write currents to drive phase transformations or filament growth, electrochemical random access memory (ECRAM) decouples the “write” and “read” operations using a “gate” electrode to tune the conductance state through charge‐transfer reactions, and every electron transferred through the external circuit in ECRAM corresponds to the migration of ≈1 ion used to store analogue information. Like static dopants in traditional semiconductors, electrochemically inserted ions modulate the conductivity by locally perturbing a host's electronic structure; however, ECRAM does so in a dynamic and reversible manner. The resulting change in conductance can span orders of magnitude, from gradual increments needed for analog elements, to large, abrupt changes for dynamically reconfigurable adaptive architectures. In this in‐depth perspective, the history of ECRAM, the recent progress in devices spanning organic, inorganic, and 2D materials, circuits, architectures, the rich portfolio of challenging, fundamental questions, and how ECRAM can be harnessed to realize a new paradigm for low‐power neuromorphic computing are discussed.

Translating the surging interest in neuromorphic electronic components, such as those based on nonlinearities near Mott transitions, into large‐scale commercial deployment faces steep challenges in the current lack of means to identify and design key material parameters. These issues are exemplified by the difficulties in connecting measurable material properties to device behavior via circuit element models. Here, the principle of local activity is used to build a model of VO₂/SiN Mott threshold switches by sequentially accounting for constraints from a minimal set of quasistatic and dynamic electrical and high‐spatial‐resolution thermal data obtained via in situ thermoreflectance mapping. By combining independent data sets for devices with varying dimensions, the model is distilled to measurable material properties, and device scaling laws are established. The model can accurately predict electrical and thermal conductivities and capacitances and locally active dynamics (especially persistent spiking self‐oscillations). The systematic procedure by which this model is developed has been a missing link in predictively connecting neuromorphic device behavior with their underlying material properties, and should enable rapid screening of material candidates before employing expensive manufacturing processes and testing procedures.

Understanding the pathways and time scales underlying electrically driven insulator-metal transitions is crucial for uncovering the fundamental limits of device operation. Using stroboscopic electron diffraction, we perform synchronized time-resolved measurements of atomic motions and electronic transport in operating vanadium dioxide (VO₂) switches. We discover an electrically triggered, isostructural state that forms transiently on microsecond time scales, which is shown by phase-field simulations to be stabilized by local heterogeneities and interfacial interactions between the equilibrium phases. This metastable phase is similar to that formed under photoexcitation within picoseconds, suggesting a universal transformation pathway. Our results establish electrical excitation as a route for uncovering nonequilibrium and metastable phases in correlated materials, opening avenues for engineering dynamical behavior in nanoelectronics.