Suyoun Lee

Transition metal‐based conductive‐bridge RAM offers superior non‐volatility and stable low‐resistance states in the MΩ resistance range compared to noble metal (Ag, Cu) counterparts; however, achieving stable multi‐level operation with low read noise under low‐current conditions remains a critical challenge. This study reports a dual‐material engineering strategy altering filament transport by combining a Ru–Pt alloy electrode with an AlO _x switching layer. Pt incorporation suppresses excessive Ru ion injection, while the low‐defect‐density AlO _x matrix stabilizes filament evolution, leading to a nanocluster‐based conductive pathway rather than a continuous metallic filament. Temperature‐dependent electrical measurements reveal thermally activated hopping conduction (Ea ∼0.44 eV), and current–voltage analysis indicates hopping distances of 1.0–1.9 nm across resistance states, confirming gradual inter‐cluster spacing modulation. This filament structure enables 16 distinct conductance states with reduced read noise over TaO _x ‐ and HfO _x ‐based devices. Incorporated into a convolutional spiking neural network framework, the device achieves 80.97% FMNIST inference accuracy (post‐training quantization only) and ultralow inference energy of 0.817 pJ/inference in the FC layer—roughly three orders of magnitude lower than Ag‐ and Cu‐based ECM systems. These results establish a direct link between materials design, filament physics, and system‐level performance, highlighting dual‐material engineering as a pathway toward energy‐efficient, high‐density analog synaptic devices.

Hopfield networks offer a hardware-friendly framework for energy-efficient associative memory, yet their practical realization in memristor crossbar arrays is critically hindered by device-to-device (D2D) variability, which prevents reliable parallel programming. Here, we address this bottleneck through systematic composition engineering of the Ge–Te solid electrolyte in conductive bridge random access memory (CBRAM) devices. By varying the Ge:Te ratio, we identify Ge3.5Te1 as an optimal composition, exhibiting the smallest average coefficient of variation in characteristic parameters compared to GeSe-based devices. Raman spectroscopy reveals that this improvement is associated with a narrower distribution of Ge-centered tetrahedral coordination environments, which we propose narrows the spread of Cu+ migration barriers and thereby renders filament formation more reproducible. Combining this electrolyte optimization with pore-size scaling to 200 nm, we fabricate a selector-free 16 × 16 Cu/Ge3.5Te1 CBRAM crossbar array and demonstrate a 4 × 4 Hopfield-type associative network capable of learning and recalling binary pattern pairs via fully parallel programming using a half-selection scheme. Successful pattern recall is achieved for up to two stored associations despite the absence of selector elements, establishing a proof-of-concept for selector-free hardware implementations of associative memory. These results highlight the critical role of electrolyte bonding structure in determining memristor uniformity and provide a materials-driven pathway toward scalable, parallel neuromorphic computing systems.

Neural circuits in the retina extract motion direction and velocity through asymmetric dendritic processing, but replicating these computations in solid‐state hardware has not been achieved. Herein, a single multi‐gate ion‐gel graphene transistor (MIG‐GFET) is demonstrated to emulate key operations of the direction‐selective ganglion cell (DSGC) and starburst amacrine cell (SAC) microcircuit. Spatially distributed gates created artificial dendritic compartments whose ionic dynamics enabled nonlinear summation of sequential inputs, yielding intrinsic direction selectivity without algorithmic post‐processing. An oppositely biased inhibitory gate reproduced SAC‐like null‐side inhibition and yielded a band‐pass velocity response with a tunable preferred speed—closely mirroring the spatiotemporal filtering properties underlying direction‐ and speed‐dependent firing in biological DSGCs. This velocity selectivity enables the MIG‐GFET to function as an analog motion filter. By suppressing ego‐motion–induced global scene jitter within a defined frequency band, the device achieves on‐sensor image stabilization with lower latency and power consumption than digital post‐processing. Therefore, dendritic‐level computation can be embedded directly into a graphene transistor, offering a path toward vision hardware where sensing and early neural processing are co‐localized at the device level. This compact, low‐power approach is compatible with focal‐plane array integration and broad applications in real‐time motion analysis.

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.

Engineering van Hove singularities (vHss) near the Fermi level, if feasible, offers a powerful route to control exotic quantum phases in electronic and magnetic behaviors. However, conventional approaches rely primarily on chemical and electrical doping and focus mainly on local electrical or optical measurements, limiting their applicability to coupled functionalities. In this study, a vHs-induced insulator-metal transition coupled with a ferromagnetic phase transition was empirically achieved in atomically designed quasi-2D SrRuO3 (SRO) superlattices via epitaxial strain engineering, which has not been observed in conventional 3D SRO systems. Theoretical calculations revealed that epitaxial strain effectively modulates the strength and energy positions of vHs of specific Ru orbitals, driving correlated phase transitions in the electronic and magnetic ground states. X-ray absorption spectroscopy confirmed the anisotropic electronic structure of quasi-2D SRO modulated by epitaxial strain. Magneto-optic Kerr effect and electrical transport measurements demonstrated modulated magnetic and electronic phases. Furthermore, magneto-electrical measurements detected significant anomalous Hall effect signals and ferromagnetic magnetoresistance, indicating the presence of magnetically coupled charge carriers in the 2D metallic regime. This study establishes strain engineering as a promising platform for tuning vHss and resultant itinerant ferromagnetism of low-dimensional correlated quantum systems.

A deep understanding of neuronal circuitry connectivity is essential to replicate biological functions effectively. Inferring neural connectivity considers the cross-correlation of spike timing. Neuromorphic systems utilize online learning algorithms that leverage the temporal correlations of spikes by utilizing spiking neural networks. This research demonstrates real-time, large-scale neural connectivity inference by implementing the presynaptic spike-driven spike-timing-dependent plasticity method on neuromorphic hardware. We validate the capability of the proposed method to perform advanced neuron connectivity inference using synthetic data generated from leaky integrate-and-fire neurons on a multi-scale. Additionally, we analyzed that the proposed method exhibits invariant high inference performance in sparse networks without burst firing, regardless of transmission delay. Finally, we demonstrate the feasibility of the proposed method in real-time neural connectivity inference in actual in vitro or in vivo contexts by conducting neural connectivity inference simulating in a way closely mirroring in vitro conditions through fluorescence imaging signal data.

A crystallinity‐controlled ZrO₂‐based memristor with localized conductive filaments along low energy pathways is proposed as a short‐term memory for reservoir computing. image

Memristors have been emerging as promising candidates for computing systems in post‐Moore applications, particularly electrochemical metallization‐based memristors, which are poised to play a crucial role in neuromorphic computing and machine learning. These devices are favored for their high integration density, low power consumption, rapid switching speed, and significant on/off ratio. Despite advancements in various materials, achieving adequate electrical performance—characterized by threshold switching (TS) behavior, spontaneous reset, and low off‐state resistance—remains challenging due to the limitations in conductance filament control within the nanoscale resistive switching layer. In this study, we introduce an efficient method to control the ZrO₂ crystallinity for tunable volatility memristor by establishing the filament paths through a simple thermal treatment process in a single oxide layer. The effect of ZrO₂ crystallinity to create localized filament paths for enhancing Ag migration and improving TS behavior is also investigated. In contrast to its amorphous counterpart, crystallized ZrO₂ volatile memristor, treated by rapid thermal annealing, demonstrates a steep switching slope (0.21 mV dec^–1), a high resistance state (25 GΩ), and forming‐free characteristics. The superior volatile performance is attributed to localized conductive filaments along low‐energy pathways, such as dislocations and grain boundaries. By coupling with enhanced volatile switching behavior, we believe that the volatility is finely tuned to function as short‐term memory for reservoir computing, making it particularly well‐suited for tasks such as audio and image recognition. image

Associative multimodal artificial intelligence (AMAI) has gained significant attention across various fields, yet its implementation poses challenges due to the burden on computing and memory resources. To address these challenges, researchers have paid increasing attention to neuromorphic devices based on novel materials and structures, which can implement classical conditioning behaviors with simplified circuitry. Herein, we introduce an artificial multimodal neuron device that shows not only the acquisition behavior but also the extinction and the spontaneous recovery behaviors for the first time. Being composed of an ovonic threshold switch (OTS)-based neuron device, a conductive bridge memristor (CBM)-based synapse device, and a few passive electrical elements, such observed behaviors of this neuron device are explained in terms of the electroforming and the diffusion of metallic ions in the CBM. We believe that the proposed associative learning neuron device will shed light on the way of developing large-scale AMAI systems by providing inspiration to devise an associative learning network with improved energy efficiency.