Logic-in-memory (LIM) architectures are explored to address the data transfer bottleneck of conventional von Neumann architectures by integrating computation directly within memory arrays. Among various candidates, memristor-based LIM systems have gained significant attention due to their non-volatile switching behavior and compatibility with dense integration. In this work, a page-wise LIM architecture is implemented using a 3D vertical resistive random-access memory array composed of self-rectifying Pt-Ta₂O₅-Al:HfO₂-TiN memristors. Two logic primitives-1 M and 2 M logic-are employed to perform intra-page and inter-page operations, respectively, enabling core Boolean functions to be executed entirely within the array through resistive state transitions. Based on these logic operations, a memristive arithmetic logic unit (mALU) is designed to perform essential arithmetic functions, including addition, subtraction, increment, and decrement. Owing to the vertical structure of vertical resistive random-access memory, a 2-bit full adder is implemented with a footprint of only three cells and completed in 12 steps. The proposed intra-and inter-page operations, along with complete mALU functionality, are experimentally demonstrated with high reproducibility. Combined with significantly reduced spatiotemporal cost, these results highlight the promise of this architecture for scalable and energy-efficient in-memory computing.

The existing physical implementations of reservoir computing are constrained mainly by time-delay architectures that lack capabilities for spatial data processing. This study presents a multifunctional memristor-based reservoir computing system, the memristive echo state network (MESN), which enables spatiotemporal computation within a single device crossbar array. Utilizing a reconfigurable Ta/HfO₂/RuO₂ memristor, three distinct switching modes are realized: stochastic for input masking, bistable for sigmoidal activation, and analog for precise readout. A full in-memory implementation is experimentally demonstrated using a one-transistor-one-resistor crossbar array integrated with indium oxide thin-film transistors. Spatial inference is validated through cellular automata, confirming reliable hardware operation. High-level simulations based on the hardware results demonstrate the performance of the proposed MESN, achieving high accuracy in predicting the Lorenz attractor and classifying attention-deficit/hyperactivity disorder. The system also predicted the Kuramoto-Sivashinsky equation, representing the first memristor-based reservoir to model complex spatiotemporal partial differential equations. These results highlight the potential of multifunctional memristor arrays for scalable in-memory spatiotemporal computing.

Single-walled carbon nanotubes and graphene hybrid films with complementary properties are combined for use in high-performance transparent electrodes and field-effect transistors (FETs). Based on the transfer characteristics for the hybrid film-based FETs, an improved Ion/Ioff and on-state current are achieved, compared with pristine graphene. Notably, the hybrid film has a sheet resistance of 300 Ω/sq with 96.4% transparency. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

A symbiotically bio-inspired complementary photoconductor is demonstrated using a well-defined silicon nanowire (SiNW) and porphyrins. The observed direction of the conductance change by light illumination has opposite signs for n- and p-type SiNWs. On the basis of this observation, it is concluded that complementary photoconductors can be used to form functional logic gates of optical input with remarkably low static power dissipation. Detailed facts of importance to specialist readers are published as ”Supporting Information”. Such documents are peer-reviewed, but not copy-edited or typeset. They are made available as submitted by the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

A photoinduced hybrid memory operating with a low voltage is demonstrated by embedding the fullerene derivative, [6,6]-phenyl-C61-butyric acid methyl ester (PCBM), into a conventional silicon-channel FET with a nanogap, upon application of both electrical and optical pulses. The nanogap geometry allows high mobility, which is the same as that of conventional silicon and organic–inorganic hybrid integration without thermal instability.

Janus materials are an emerging class of two-dimensional materials with a diversity of two exclusive sides, which embark on various new multifunctional properties for electronics, optoelectronics, and memory application devices. Evolving technologies like neuromorphic computing based on floating-gate transistors, architecting an advanced artificial intelligence technology (AIT) to emulate efficient brain-like synaptic functions. In this study, we present an emerging memory design using Au/hBN/WSSe and Gr/hBN/WSSe heterostructures on the same WSSe channel, where gold and graphene serve as floating-gate materials and hexagonal boron nitride (h-BN) as an effective tunneling layer. By comparing the performance metrics based on device configurations under controlled conditions, we achieved a current ON/OFF ratio (∼10 5 ) and (∼10 3 ) for Au and few layer graphene as floating gates, respectively. The memory devices with Gr floating gate demonstrated the significant and consistent memory window of ΔV = 65 V compared to Au (ΔV = 51 V). Further, Gr/hBN/WSSe showed promising endurance (10 5 cycles) and retention (10 6 s), having gate-dependent multi-states for erase and program. Moreover, we used an artificial neural network (ANN) for digit-MNIST and Fashion-MNIST simulations, which achieved 87 % and 78 % accuracy, respectively. Simulations of WSSe-based synaptic transistors further demonstrate their capability to support ANN learning, underscoring the potential of this platform to drive next-generation AIT for memory and computing systems.

This research proposes a simulation dataset that reflects the operational characteristics of submarine infrared sensors in maritime environments. Although infrared imaging is widely used for covert observation and surveillance, image quality often degrades due to transmittance attenuation caused by submarine sapphire optical windows and the inherent limitations of Noise Equivalent Temperature Difference (NETD) characteristics. To address this, a realistic dataset was constructed using Radiation Transfer Modeling (RTM), incorporating sapphire optical window transmittance values (0.65 and 0.89) and simulating thermal noise across various NETD levels (10, 20, and 30 mK). Using the generated dataset, noise reduction and super-resolution experiments were performed to evaluate the limitations of existing image enhancement techniques. The proposed dataset offers a standardized benchmark for infrared image enhancement research in specialized underwater environments and enables more accurate prediction of image processing performance under real submarine operating conditions.

This work presents an electrically tunable physical reservoir computing (RC) system using two-terminal Pt/HfO2/TiN memristors. The device’s intrinsic relaxation time is effectively increased by over two orders of magnitude by modulating the base voltage between data pulses, enabling robust temporal data encoding for pulse intervals ranging from 0.14 to 100 ms. Enhanced separability is experimentally demonstrated through the classification of the Neuromorphic-MNIST (N-MNIST) dataset, surpassing the performance of conventional RCs without base voltage modulation. Frequency-domain analysis using multiple superimposed oscillator inputs shows controllable low-pass filtering characteristics. At the same time, the chaotic time-series prediction of the Mackey–Glass dataset achieves a normalized root mean square error of 0.27 without input data modulation and teaching signal for 200 timesteps. These results illustrate a feasible pathway toward tunable RC systems that achieve high performance while keeping the simplicity of a two-terminal device architecture.

Accurate extraction of mobility parameters in two-dimensional (2D) transition metal dichalcogenide (TMD)-based field-effect transistors (FETs) is crucial for evaluating their performance and optimizing device design. Conventional mobility extraction methods such as the field-effect mobility approach suffer from inaccuracies owing to the influence of series resistance and noise amplification. In this paper, we present an advanced polynomial Y-function methodology for the precise mobility characterization of MoS₂ FETs. This methodology enables a systematic discrimination among various scattering mechanisms while precisely extracting the threshold voltage. Through a comparative analysis of back-gate (BG) and top-gate (TG) MoS₂ FET configurations, we demonstrated the superior accuracy and consistency of the proposed method compared with conventional approaches. The results revealed that the TG-FET exhibited stronger surface-roughness scattering owing to the intensified transverse electric field from the thinner dielectric layer, leading to pronounced mobility degradation. The polynomial Y-function method successfully isolates key degradation factors, thereby enabling a more comprehensive understanding of the carrier transport mechanisms in 2D FETs. These findings provide a robust framework for optimizing 2D material-based electronic devices, facilitating their integration into next-generation nanoelectronic applications.

Logic-in-memory (LIM) architectures are explored to address the data transfer bottleneck of conventional von Neumann architectures by integrating computation directly within memory arrays. Among various candidates, memristor-based LIM systems have gained significant attention due to their non-volatile switching behavior and compatibility with dense integration. In this work, a page-wise LIM architecture is implemented using a 3D vertical resistive random-access memory array composed of self-rectifying Pt-Ta₂O₅-Al:HfO₂-TiN memristors. Two logic primitives-1 M and 2 M logic-are employed to perform intra-page and inter-page operations, respectively, enabling core Boolean functions to be executed entirely within the array through resistive state transitions. Based on these logic operations, a memristive arithmetic logic unit (mALU) is designed to perform essential arithmetic functions, including addition, subtraction, increment, and decrement. Owing to the vertical structure of vertical resistive random-access memory, a 2-bit full adder is implemented with a footprint of only three cells and completed in 12 steps. The proposed intra-and inter-page operations, along with complete mALU functionality, are experimentally demonstrated with high reproducibility. Combined with significantly reduced spatiotemporal cost, these results highlight the promise of this architecture for scalable and energy-efficient in-memory computing.

The existing physical implementations of reservoir computing are constrained mainly by time-delay architectures that lack capabilities for spatial data processing. This study presents a multifunctional memristor-based reservoir computing system, the memristive echo state network (MESN), which enables spatiotemporal computation within a single device crossbar array. Utilizing a reconfigurable Ta/HfO₂/RuO₂ memristor, three distinct switching modes are realized: stochastic for input masking, bistable for sigmoidal activation, and analog for precise readout. A full in-memory implementation is experimentally demonstrated using a one-transistor-one-resistor crossbar array integrated with indium oxide thin-film transistors. Spatial inference is validated through cellular automata, confirming reliable hardware operation. High-level simulations based on the hardware results demonstrate the performance of the proposed MESN, achieving high accuracy in predicting the Lorenz attractor and classifying attention-deficit/hyperactivity disorder. The system also predicted the Kuramoto-Sivashinsky equation, representing the first memristor-based reservoir to model complex spatiotemporal partial differential equations. These results highlight the potential of multifunctional memristor arrays for scalable in-memory spatiotemporal computing.

Single-walled carbon nanotubes and graphene hybrid films with complementary properties are combined for use in high-performance transparent electrodes and field-effect transistors (FETs). Based on the transfer characteristics for the hybrid film-based FETs, an improved Ion/Ioff and on-state current are achieved, compared with pristine graphene. Notably, the hybrid film has a sheet resistance of 300 Ω/sq with 96.4% transparency. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

A symbiotically bio-inspired complementary photoconductor is demonstrated using a well-defined silicon nanowire (SiNW) and porphyrins. The observed direction of the conductance change by light illumination has opposite signs for n- and p-type SiNWs. On the basis of this observation, it is concluded that complementary photoconductors can be used to form functional logic gates of optical input with remarkably low static power dissipation. Detailed facts of importance to specialist readers are published as ”Supporting Information”. Such documents are peer-reviewed, but not copy-edited or typeset. They are made available as submitted by the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.