Suhas Kumar

Deep-ultraviolet (DUV) sensing underlies safety-critical systems, especially flame and combustion detection, where detectors should both respond fast and reject false alarms from sources such as welding arcs. Conventional detectors offer no usable in-sensor dynamics, since trap-free devices offer a static response, while those laden with often-deep-level traps offer dynamics viewed as delaying a steady-state response. Thus, discriminating temporal features is deferred to downstream processing, forcing a trade-off between response time and accuracy. Here, in β-Ga2O3, we exploit such traps – DUV illumination photoactivates occupied traps, the Poole–Frenkel emission of which results in a current-controlled negative differential resistance (NDR). The NDR is out-of-equilibrium, existing only during photoactivation of traps away from a steady state. Coupled to a small external capacitance, this instability sustains voltage firing that is band-pass in effective dose rate. Reading the response at five different tuning currents distinguishes six DUV sources of identical wavelength, differing only in temporal modulation, including welding and flame that are indistinguishable by average intensity. This method offers 98.3% accuracy at 100 seconds versus 87.4% for an ideal intensity-only detector, thus discriminating dynamical information at the point of sensing versus by threshold-only. Therefore, defect dynamics treated as delaying the material’s actual response, can encode useful information.

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.

Identifying molecular structure based on spectroscopic readings is a key task in a variety of chemical and biological applications. Common spectroscopy techniques, such as Infrared (IR) Spectroscopy and Mass Spectrometry (MS), provide detailed information on the structure of molecular compounds but nonetheless require expert-level knowledge to decode. Machine learning has emerged as a potential solution for automating structure prediction from chemical spectra; however, current approaches generally focus on single sensor modalities, neglecting to leverage the complementary information contained within differing spectra. In this paper, we introduce Peak2Patch, a novel approach to fusion-enhanced prediction of functional groups from IR and mass spectra. First, we perform a detailed comparison of backbone networks for encoding both sparse mass spectra and dense IR spectra and demonstrate the superior performance of transformer neural networks over current state-of-the-art convolutional neural networks. Second, we evaluate three broad categories of fusion: early (raw feature), middle (deep feature), and late (decision) fusion, demonstrating the potential of a deep feature fusion-based approach. Lastly, we present Peak2Patch, our attention-based fusion scheme, which leverages cross-attention to mix features between encoded tokens of the two modalities. We validate our approach on a publicly available multimodal spectroscopic data set of 790k simulated molecules, demonstrating a large improvement in functional group prediction over both the previous state-of-the-art and our own strong single-modal baselines.

Associative memories, especially those based on low-energy analog hardware, offer a promising primitive to handle the memory dominated era of large artificial intelligence (AI) models and in-sensor/edge intelligence. However, a critical but unsolved limitation of such systems is their iterative updates, which accumulate the native hardware noise, in addition to consuming high energy and latency. While emerging attention-equivalent softmax-based dense associative memories (SDAMs) reduce this cost through single-step recall, their use of dot-product similarity not only imposes hardware complexity due to the need for bipolar information but also suffers from obfuscation of closely related patterns during retrieval. Here, we introduce distance-aware in-memory SDAM (DIM-SDAM) implemented on foundry-manufactured 256 × 256 transposable memristor crossbars via algorithm-hardware co-optimization. The distance-based similarity operates with unipolar inputs (thus reducing hardware overhead by 50%), while preserving single-step convergence that minimizes accumulation of analog hardware noise. DIM-SDAM reconstructs Fashion-MNIST with near-perfect accuracy for corrupted inputs and further retrieves 100 EMNIST patterns with >95% accuracy, overcoming the linear storage capacity limit of Hopfield networks. DIM-SDAM also outperforms GPUs by 25× in energy efficiency, opening a pathway toward scalable, energy-efficient associative memories.

Vanadium dioxide (VO2) is of interest for adaptive electronic applications such as neuromorphic neuristor devices and variable emissivity or tunable thermal control materials, thanks to its key property—a metal–insulator transition (MIT) at 68 °C that is accompanied by a dramatic change in electrical and optical properties. To improve performance in these roles, it is critical to develop approaches to engineer transport properties and the MIT behavior. While many documented techniques exist to modulate the MIT and film resistivities via lattice strain and chemical doping, less is known about the effects of ion irradiation on the intrinsic properties of VO2, despite the ability to control the spatial distribution of irradiation beams and the prevalence of high energy ion implantation in the semiconductor industry. The impact of irradiation of different acceleration energies on the responses of VO2 is of specific interest, as charged particle energy generally impacts both the resulting defect profile and corresponding transport behavior. Here, we demonstrate that 2 MeV He ions at equivalent calculated displacements per atom, in two different types of films, can create remarkable changes to the nature of charge transport in VO2, especially in the low-temperature insulating phase. Simulation of resulting changes in electrical conductivity reveals that He ion irradiation offers a strategy to increase both oscillation frequency and the signal transmission. These results provide insights into the intentional design of defect populations to modulate transport for neuromorphic VO2 devices.

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.

Over the past decade, dendrites of neurons, which were previously thought to perform only information pooling and networking, have now been shown to express complex temporal dynamics, Boolean-like logic, arithmetic, signal discrimination, and edge detection. Mimicking this rich functionality could offer a powerful primitive for neuromorphic computing. Here, using Ovonic threshold switching in Sb-Te-doped GeSe, we demonstrate a single two-terminal component capable of self-sustained dynamics and universal Boolean logic in addition to XOR operations (which is traditionally thought to require a network of active components). We then employed logic-driven dynamics to detect and estimate the gradients of edges in images. The Ovonic switch exhibits properties of a half adder and a full adder in addition to discriminative logic accommodating inhibitory and excitatory signals. We show that this simple computational primitive offers a highly improved energy efficiency. As such, this work paves the path for potentially emulating dendrites for efficient postdigital neuromorphic computing.

Managing heat is a major challenge in modern silicon-based computers due to both large static and dynamic power dissipations. There is a growing perspective that heat can serve as an information carrier (instead of being treated as a useless by-product) in post-silicon devices, enabling new functions and on-chip energy recycling. In this review, we introduce how heat can be utilized as a degree of freedom in electronic devices, and how such devices may enable efficient computers.