There is broad interest in layered, two-dimensional (2D) semiconductors owing to their high optical absorption and ability to heterogeneously integrate with various substrates. In such layered semiconductors, mesoscopic imperfections, such as surface step edges, may affect charge carrier kinetics and device performance, but studying their effects on carrier transport with sufficient spatial and temporal resolution is a challenge. Using scanning ultrafast electron microscopy, we image the near-surface charge carrier kinetics with mu m/ps spatiotemporal resolution in single-crystal MoS2 samples with surface steps tens of nanometers in height. We find an approximately 3-fold increase in surface trap density near such surface steps compared to the uniform, flat surface regions. Despite the difference in trap state density, filling of the traps occurs over the same time scale (similar to 100 ps), but carrier recovery is much slower near the step compared to the uniform regions of the material. These results provide an approach to measure ultrafast dynamics and determine the required uniformity of layered materials depending on the speed limit constraints for specific applications.

Our growing computing needs, especially in applications that heavily rely on artificial intelligence (AI), motivate a search for new components that could substantially augment the performance of general-purpose digital computers. Beyond ON/OFF switching, new components with linear multistate analog resistive tuning, nonlinear volatile switching, spiking, oscillatory, stochastic and other complex functionalities could enable highly efficient neuromorphic computing schemes for AI information processing. Compared to the extreme multifunctionality of biological neurons, realizing all the above characteristics in a single, scalable analog component remains a grand challenge. Here we investigate electrochemical gating combined with localized thermal activation to program and switch a single, vertically integrated and dimensionally scaled electrothermal chemical random access memory (ETCRAM) with a channel and reservoir composed of phase-separated vanadium oxide. Closely related to electrochemical RAM (ECRAM), ETCRAM uses an integrated gate-heater electrode to overcome kinetic barriers that help retain states at ambient temperatures. In addition to synapse-like stable and programmable analog resistance states arising from redox-tunable phase coexistence, a single component exhibits neuron-like nonlinear conductance switching with a tunable threshold and self-driven dynamics owing to the thermally driven metal-insulator phase transition in vanadium dioxide. More broadly, we demonstrate that electrochemically stabilized phase coexistence could unlock analog electronics with novel functionality, stability, reconfigurability, and scalability.

There is growing interest in correlated oxides that can switch between volatile resistance states when an electrical bias is applied, functioning as artificial neurons in neuromorphic computing systems. Most devices typically rely on first-order insulator-metal transitions (IMT). However, recent discoveries have shown that devices made of a second-order spin-transition material, such as LaCoO3 (LCO), can exhibit different or improved functionalities. Despite their significance, the microscopic details surrounding the formation of conductive channels have still been unreported. In this study, the spatiotemporal details of channel formation are revealed by using a combination of infrared (IR) and Raman microscopy. Comparison of LCO and materials such as VO2 reveals critical differences with important ramifications for computing. First, the findings indicate that LCO channels are narrower and more efficient than VO2, but they are also more sensitive to electric fields and disorder. Channels are found to repeatedly hop between different locations under steady-state oscillations, a behavior not previously reported. Additionally, memory effects at high bias are observed. The experiments, along with finite element simulations (FES), suggest that the spin transition in LCO may significantly influence channel nucleation, leading to an increased sensitivity of neuronal devices to disorder and electrode geometry. We discuss how the inherent stochasticity and memory effects could enable functionalities in neuromorphic computing.

A programmable linear resistor has been missing from the toolkit of modern microelectronics. Here, we introduce a non-volatile resistive device based upon a self-heated, metal-oxide electrochemical cell which we call ‘electro-thermo-chemical random access memory’ (ETCRAM) 1 . The key advance is an electrothermal gate that simultaneously spreads heat and oxygen vacancy reactions during programming to enable a large, reversible composition modulation with ×10 9 tunable analog resistance. The self-heating profoundly reduces noise, with ETCRAM having 100× lower conductance errors than other resistive memory which enables thousands of distinguishable analog states. The unique current-voltage linearity enables a wide range of signal processing tasks, including dynamic-gain amplification, reconfigurable voltage division, and vector-matrix multiplication (VMM). We demonstrate a CMOS-integrated ETCRAM array based upon an interposer substrate, and utilize it for high-performance analog signal processing. The unique combination of linearity, range, and precision of ETCRAM enables new possibilities for electrochemical memory in analog signal processing, edge computing, and artificial intelligence accelerators. [1] Gross, A.L., et al. Self-heating electrochemical memory for high-precision analog computing. arXiv:2505.15936 (2025)

In recent years, there has been an increase in the research interest in correlated oxides, partially due to their ability to transition between volatile resistance states when an electrical bias is applied. This property makes these materials suitable for artificial neuron design towards neuromorphic computing systems. While first-order insulator-metal transition (IMT) materials, such as VO 2 , have historically dominated the field, recent findings have revealed that a second-order spin transition, like the one present in LaCoO 3 (LCO), can also exhibit volatile switching behavior with new and improved functionalities. Despite the extensive research efforts directed towards device operation and the macroscopic effects observed, the microscopic mechanisms underlying the formation of conductive channels that enable device operation remain to be fully elucidated. We present spatiotemporal studies of channel formation in operando current driven LCO artificial neurons using a combination of infrared (IR) and Raman microscopies. These complementary experiments, which employ high spatial resolution Raman spectroscopy and high temporal resolution IR-imaging, are capable of elucidating the switching mechanisms, even in the context of fast self-oscillatory regimes. Integrating these results with current-voltage characteristics, we are able to establish a complete correlation between material local changes and device behavior. Furthermore, the interpretation of the spectroscopy information provides novel insights into the material's evolution. A comparison of LCO and materials such as VO 2 reveals qualitative differences. The findings indicate that LCO channels exhibit increased sensitivity to electric fields and disorder in comparison to VO 2 . It has been observed that channels repeatedly hop between distinct locations during steady-state oscillations, a phenomenon that has not been previously reported. We present experiments along finite element simulations, which were utilized to develop and validate a model that supports our hypothesis. The results of this study indicate that the spin transition in LCO may have a substantial impact on channel nucleation, enhancing the sensitivity of neuronal devices to disorder and electrode geometry. The crossover regime present in LCO, where high spin and low spin thermal fluctuations dominate the energy landscape, constitutes a fundamentally different scenario from a first order phase coexistence. During periods of current spiking, this sensitive energy landscape gives rise to a probabilistic competition between non-simultaneous filaments in the oscillatory negative differential resistance (NDR) regime or upon consecutive switching events. We present direct observation of the distribution of filaments in micron-scale devices and perform electrode engineering to tune the probability distribution using uniform and non-uniform electric field geometries. We show that fully volatile regimes are accompanied by short- and long-term memory effects at higher current bias. These semi-permanent changes in the material have the potential to be exploited for memory applications. These results highlight the role of stochasticity and material-specific details for exploiting spin-crossover materials in neuromorphic applications.

All-optical computing promises fast, energy-efficient processing and has regained momentum as artificial intelligence workloads strain electronic hardware. Among optical approaches, free-space diffractive optics enables rapid, multidimensional information processing with high parallelism, but advancing these systems requires a fundamental understanding of their computational capacity and how to exploit it. Whereas electronic computation relies on cascaded linear and nonlinear operations to implement digital circuits, neural networks, and machine vision, we show that diffractive optical systems can implement complex logic circuits by collapsing serial chains of nonlinear functions into a single optical stage. We first realize all basic logic gates and then demonstrate parallel half-/full adders and subtractors with one-stage readout. We further demonstrate an 8-bit ripple-carry adder by cascading eight one-shot optical full adders. Finally, we show scalability to hundreds of parallel inputs and direct two-dimensional image processing using spatially localized nonlinear optical logic functions.

The migration of crystallographic defects dictates material properties and performance for a plethora of technological applications. Density functional theory (DFT)-based nudged elastic band (NEB) calculations are a powerful computational technique for predicting defect migration activation energy barriers, yet they become prohibitively expensive for high-throughput screening of defect diffusivities. Without introducing hand-crafted (i.e., chemistry- or structure-specific) descriptors, we propose a generalized deep learning approach to train surrogate models for NEB energies of vacancy migration by hybridizing graph neural networks with transformer encoders and simply using pristine host structures as input. With sufficient training data, computationally efficient and simultaneous inference of vacancy defect thermodynamics and migration activation energies can be obtained to compute temperature-dependent vacancy diffusivities and to down-select candidates for more thorough DFT analysis or experiments. Thus, as we specifically demonstrate for potential water-splitting materials, candidates with desired defect thermodynamics, kinetics, and host stability properties can be more rapidly targeted from open-source databases of experimentally validated or hypothetical materials.

The high-temperature spin and electronic transitions in LaCoO3 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 LaCoO3 at device-relevant temperatures (25-325 °C) over length scales of tens of nanometers using STEM-EELS. This measurement is enabled by an Al2O3 coating, which prevents unwanted reduction of the LaCoO3 specimen at high temperature and vacuum. The detailed understanding of LaCoO3 transition dynamics enabled by such measurements will be crucial for optimizing LaCoO3-based neuromorphic devices and increasing reliability for real-world application.

We demonstrate a free-space optical system for realizing one-shot complex logic circuits using a reconfigurable diffractive element. We generate high-fidelity complex wave-fronts by engineering strong interference between multiple optical beams to obtain various logic functions.

Artificial intelligence (AI) is pushing the limits of digital computing to such an extent that, if current trends were to continue, global energy consumption from computation alone would eclipse all other forms of energy within the next two decades. One promising approach to reduce energy consumption and to increase computational speed is in-memory analog computing. However, analog computing necessitates a fundamental rethinking of computation at the material level, where information is stored as continuously variable physical observables. This shift introduces challenges related to the precision, dynamic range, and reliability of analog devices - issues that have hindered the development of existing memory technology for use in analog computers. Here, we address these issues in the context of memory which stores information as resistance. Our approach utilizes an electrochemical cell to tune the bulk oxygen-vacancy concentration within a metal oxide film. Through leveraging the gate contact as both a heater and source of electrochemical currents, kinetic barriers are overcome to enable a dynamic range of nine decades of analog tunable resistance, more than 3,000 available states, and programming with voltages less than 2 V. Furthermore, we demonstrate deterministic write operations with high precision, current-voltage linearity across six decades, and programming speeds as fast as 15 ns. These characteristics pave the way toward low-power analog computers with potential to improve AI efficiency by orders of magnitude.

Phase coexistence in nanoscale electrochemical random-access memory (ECRAM) has recently been demonstrated to enable both information storage and extraordinary reconfigurability. These proof-of-principle demonstrations have left the mechanistic details of such a process unresolved. Particularly, the mechanisms that stabilize the multiple phases, and the underlying processes behind sustained memory retention, remain unclear, and are necessary to design such devices. Here we report microscale ECRAM devices composed of VOx, which enables us to directly probe the active region in an operando fashion using optical techniques. Using Raman mapping, we show the phase coexistence driven by the electrochemical injection of O vacancies to be spatially uniform (i.e., with no filaments). The stability was observed to be unusually long, with 1% loss over 14 years in ambient conditions. First-principles calculations of the oxygen vacancy formation energies in VOx further support the thermodynamic coexistence of multiple VOx phases and clarify the origin of the observed long-term retention in the ECRAM devices. Further, we demonstrate single devices that can be voltage programmed to exhibit synaptic, neuronal, and reconfigurable logic gate functionalities. Therefore, we not only uncover the phase coexistence mechanism that may help device design, but also demonstrate the circuit-level applications of reconfigurability.

Non-von Neumann computing using neuromorphic systems based on analogue synaptic and neuronal elements has emerged as a potential solution to tackle the growing need for more efficient data processing, but progress toward practical systems has been stymied due to a lack of materials and devices with the appropriate attributes. Recently, solid state electrochemical ion-insertion, also known as electrochemical random access memory (ECRAM) has emerged as a promising approach to realize the needed device characteristics. ECRAM is a three terminal device that operates by tuning electronic conductance in functional materials through solid-state electrochemical redox reactions. This mechanism can be considered as a gate-controlled bulk modulation of dopants and/or phases in the channel. Early work demonstrating that ECRAM can achieve nearly ideal analogue synaptic characteristics has sparked tremendous interest in this approach. More recently, the realization that electrochemical ion insertion can be used to tune the electronic properties of many types of materials including transition metal oxides, layered two-dimensional materials, organic and coordination polymers, and that the changes in conductance can span orders of magnitude has further attracted interest in ECRAM as the basis for analogue synaptic elements for inference accelerators as well as for dynamical devices that can emulate a wide range of neuronal characteristics for implementation in analogue spiking neural networks. At its core, ECRAM shares many fundamental aspects with rechargeable batteries, where ion insertion materials are used extensively for their ability to reversibly store charge and energy. Computing applications, however, present drastically different requirements: systems will require many millions of devices, scaled down to tens of nanometers, all while achieving reliable electronic-state tuning at scaled-up rates and endurances, and with minimal energy dissipation and noise. In this review, we discuss the history, basic concepts, recent progress, as well as the challenges and opportunities for different types of ECRAM, broadly grouped by their primary mobile ionic charge carrier, including Li, protons, and oxygen vacancies.

Anything and everything can be solved with light—but under what contexts should one use optical computing and how should optical systems be designed for these tasks? In the shadow of reaching physical limits to traditional computer scaling and the large demands from artificial intelligence, research efforts have focused upon a range of optical computing pursuits as of late. This has included advances across a range of approaches, including free‐space and on‐chip implementations. Amid this excitement, key considerations beyond how to generally harness optical principles to enable computation, are what computations to advantageously pursue and how to design optical systems for these tasks. In particular, this perspective considers free‐space optical computing informed by recent research findings to consider select topics, including scene classification, integral differential equations, and many‐body simulations. In these contexts, this perspective considers what computations optical computing can and should enable and argues that a codesign approach whereby materials, devices, architectures, and algorithms are simultaneously optimized is needed considered for best performance.

The insertion of electron-donating ions has emerged as a powerful technique to manipulate the electronic structure of correlated oxides. However, the resulting electronic structure remains poorly understood, with challenges in quantifying dopant concentration, unexplained differences with substitutionally doped films, and a poor understanding of how dopant atoms interact with insulator-metal transitions (IMTs). Here, these issues are addressed in the context of the rare earth nickelates, a prototypical correlated oxide family with widely tunable electronic behavior under the insertion of protons and alkali metals as interstitial dopants. RNiO3 (R = Pr, Nd) epitaxial thin films are synthesized, lithium dopants are introduced and quantified using electrochemical and synchrotron-based techniques, and the resulting electronic structure is studied. From electronic transport measurements of LixLiRNiO3, lithium is found to affect the metal-insulator transition, causing more than an order of magnitude reduction in ground-state resistivity at fractions xLi < 0.18, a systematic lowering of transition temperature, and successively smaller ON/OFF ratios over 0.00 < xLi < 0.25. At larger fractions xLi > 0.25, the transition is destroyed, and insulating behavior is observed over T = 5-300 K. Angle-resolved photoemission (ARPES) confirms transport results and reveals band renormalization occurring over 0.10 < xLi ≤ 0.71. ARPES and X-ray absorption spectroscopy (XAS) combined with density functional theory indicate that rigid band filling models are generally insufficient to explain doping from lithium, especially at low temperatures, but could approximate room temperature effects in the low doping regime (xLi < 0.10). Broadly, the results indicate that interstitial dopants lead to complex interactions with metal-insulator transitions and the emergence of an exciting family of correlated electronic phases.

Silicon-based microelectronics are limited to '150 degrees C and therefore not suitable for the extremely high temperatures in aerospace, energy, and space applications. While wide-band-gap semiconductors can provide high-temperature logic, nonvolatile memory devices at high temperatures have been challenging. In this work, we develop a nonvolatile electrochemical memory cell that stores and retains analog and digital information at temperatures as high as 600 degrees C. Through correlative scanning transmission electron microscopy, we show that this high-temperature information retention is a result of composition phase separation between the oxidized and reduced forms of amorphous tantalum oxide. This result demonstrates a memory concept that is resilient at extreme temperatures and reveals phase separation as the principal mechanism that enables nonvolatile information storage in these electrochemical memory cells.