Uniform and gradual conductance modulation is a critical requirement for memristive synaptic devices. The conventional HfO2-based memristive devices often suffer from stochastic conductive filament formation and abrupt conductance updates, resulting in large device-to-device and cycle-to-cycle variability as well as nonlinear and asymmetric synaptic weight modulation. Therefore, stabilizing and localizing conductive filament formation is essential for reliable analog switching and synaptic operation. In this work, oxygen vacancy rich ZnO is utilized as an interlayer to study its effects on analog resistive switching of a tri-layer HfO2/ZnO/HfO2 synaptic device. The ZnO layer deposited over HfO2 films exhibited significantly enhanced crystallinity compared to ZnO single-layer films. As a result, the tri-layer HfO2/ZnO/HfO2 synaptic devices exhibited markedly improved analog switching performance, including reduced nonlinearity in long-term potentiation and depression (LTP/LTD), and an asymmetric ratio approaching unity. A comparison with a bi-layer ZnO/HfO2 device having the same total oxide thickness confirmed that the observed improvements originate from the structural confinement of ZnO between two HfO2 layers, rather than from thickness effects alone. The tri-layer device also demonstrated stable multilevel retention under different compliance currents for 104 s and achieved 106 cycles of AC endurance. In addition, the devices successfully emulated biologically relevant synaptic plasticity behaviors, such as paired-pulse facilitation (PPF) and spike-timing-dependent plasticity (STDP). Furthermore, MNIST pattern recognition simulation using the device parameters yielded a classification accuracy of 92.87%. The enhanced switching characteristics of the Pd/HfO2/ZnO/HfO2/Pd device are attributed to the oxygen-vacancy-rich ZnO interlayer sandwiched between the HfO2 layers, which is proposed to stabilize confined oxygen ion migration during the repeated formation and rupture of conductive filaments under continuous switching cycles. These findings provide practical guidelines for the design and implementation of reliable synaptic devices for neuromorphic neural network applications.

ZnO-based synaptic devices employing large-bandgap materials-HfO2/ZnO and ZrO2/ZnO-have been proposed for high-performance synapse applications. In this study, bilayer HfO2/ZnO and ZrO2/ZnO synaptic devices were fabricated on an 8-in. complementary metal-oxide semiconductor (CMOS) integrated circuit platform to address the key challenges of synaptic devices, including their variability, reliability, biological synaptic functions, and integration into neuromorphic chips. A 64 & times; 64 (4096 devices) one-transistor, one-resistor (1T1R, R: synaptic device) array chip with DEMUX and input/output circuits was fabricated, and the synaptic devices within the array were thoroughly analyzed. Leveraging CMOS technology, the array exhibited +/- 1.4% variability in selection transistors, confirming that the intrinsic characteristics of the synaptic devices could be evaluated without interference from the selection transistors. Moreover, the bilayer synaptic devices, integrated on 8-in. wafers, demonstrated excellent recognition accuracy, supported by the linearity and symmetry of their long-term potentiation/depression characteristics. Notably, a critical feature for practical utilization in 64 & times; 64 (4096 devices) 1T1R array chips was achieved by modulating the recognition of input patterns. An analog synaptic device neural network achieved an accuracy comparable to that of state-of-the-art digital CMOS systems, with the potential for major improvements in speed-energy efficiency for high-performance spiking neural network and deep neural network applications.

In this study, the structure with a graphene interlayer in a ZnO/HfO2 bilayer Resistive Random-Access Memory (RRAM) is proposed. Two types of devices were fabricated to compare the effect of graphene interlayer: one with a TiN/Ti/graphene Top Electrode (TE) device and another with a TiN/Ti TE device. The condition of the singlelayer graphene was confirmed using Raman spectroscopy. The TiN/Ti/graphene TE device demonstrates an enhanced uniformity of both the set voltage (Vset) and reset voltage (Vreset). The window for the switching voltage decreases by 2.1 V, and the average on/off ratio increases by 20.09 times. The structure also exhibits self-compliance characteristics. These results highlight both the advantages of the graphene interlayer and the oxygen-scavenging properties of TiN/Ti TE. Detailed mechanisms of oxygen ion blocking by graphene interlayer are analyzed using the standard Gibbs free energy of formation (Delta Gf degrees). The switching characteristics are analyzed by the structure's work function. And the conduction mechanism is analyzed by the space-charge limited current (SCLC), related to traps.

Monitoring of ammonia (NH3) is essential for environmental, human, and industrial equipment safety. Transition metal dichalcogenide (TMD) materials, particularly Molybdenum disulfide (MoS2), have emerged as promising alternatives to conventional metal oxide-based chemiresistive sensors for room-temperature gas detection. However, further improvements in sensitivity and selectivity are still required. In this work, Cu-Silicoaluminophosphate-34 (Cu-SAPO-34) functionalized MoS2 chemiresistive sensor was designed, where MoS2 sensor surface were fabricated by chemical vapor deposition and photolithography followed by functionalization with Cu-SAPO-34 for selectivity by spray coating. Cu-SAPO-34 is a catalyst adsorbing NH3 to promote NOx reduction, and this selectivity for NH3 is leveraged in this work: The reducing behavior of NH3 promotes electronic conductivity of MoS2 resulting in a change in sensor resistance. The response increases, with an allometric trend, when the concentration of NH3 increases. Response of Cu-SAPO-34 functionalized MoS2 for 5 ppm NH3 was similar to 9 times higher than pristine MoS2. The excessive surface area of SAPO-34, and high affinity of Cu2+ ions for NH3 resulted in higher sensitivity. The sensor detected NH3 as low as 1 ppm with a response of 23 +/- 0.6% for an exposure time of 600 s at room temperature. The sensor also exhibited distinct response behavior toward NH3 in the presence of H2S and NO2, indicating its potential utility as a component in room-temperature gas sensor array systems for environmental monitoring, healthcare diagnosis, and industrial applications. Also, the active sensing area less than 2 mm & times; 2 mm makes it compatible with miniaturized sensor integration and portable applications.

Oxide-based memristive synaptic devices are promising hardware elements for neuromorphic computing, but stochastic resistive switching caused by uncontrolled defect distributions and localized conductive paths remains a critical challenge. Here, we demonstrate Zr-doped ZnO memristive synaptic devices fabricated using an atomic layer deposition (ALD) super-cycle process to regulate both defect states and surface morphology. X-ray photoelectron spectroscopy revealed a decrease in the VO-related O 1s component from 34.9% to 23.2%, while atomic force microscopy showed a reduction in RMS surface roughness from 0.7532 to 0.2603 nm with less distinguishable surface-grain features. These results suggest that Zr incorporation suppresses VO-related defects and reduces grain-boundary-assisted localized conduction. Compared with the undoped device, the Zr-doped device exhibited improved switching uniformity, reduced resistance-state variation, stable pulse endurance, and retention for over 104 s at 25, 50, and 75 °C. The regulated defect distribution and surface morphology enabled gradual and reproducible conductance modulation during long-term potentiation and depression, resulting in improved synaptic linearity. Neural network simulations using experimentally extracted LTP/LTD nonlinearity parameters further demonstrated enhanced recognition accuracy in ANN and CNN classification tasks.

This study investigates effect of the top electrode material on the resistive switching and synaptic properties of HfO2/ZnO-based memristive devices. By varying the TE materials (Pd, Ti, TiN, and TiAu), we analyzed influence of the work function difference between top and bottom electrodes on electrical performance. Devices with a larger work function difference exhibited lower forming and set voltages, which is attributed to the enhanced internal electric field. Despite this, all of the devices showed similar conduction mechanisms, with the Ti device demonstrating gradual reset behavior due to its low Gibbs free energy. Synaptic characteristics including long-term potentiation (LTP) and long-term depression (LTD) were evaluated using a pulse scheme, that confirmed stable analog conductance modulation. These results suggest that selecting an appropriate TE material can improve switching efficiency as well as synaptic reliability of the memristive devices.

This paper investigated the electrical characteristics of Ta2O5-based synaptic devices by designing and fabricating a device incorporating a ZnO layer, based on critical parameters for learning in neural network-based algorithms. A comparative analysis was conducted between synaptic devices based on a single and double layer. The results show that inserting ZnO into the Ta2O5-based synaptic device decreases the SET/RESET voltages. Furthermore, the triple-layer in synaptic devices improved yield, switching behavior uniformity, and conductance change uniformity in response to pulse inputs. These findings suggested that the triple-layer structure provided advantages in consistently forming oxygen vacancy-based conductive filaments (CFs) and allowed for better-controlled migration of oxygen ions from a structural perspective.

Correction for 'Recent advances in nanoporous NOx gas sensors: synergizing Raman spectroscopy, IoT, and machine learning for high-performance detection' by Vikas Yadav, et al., Nanoscale, 2025, https://doi.org/10.1039/d5nr01757b.

Synapse devices, pivotal components in neuromorphic computing, demonstrate unique properties that are essential for advanced computing systems. These devices, characterized by their metal/resistive layer/metal structure, rely heavily on active layer material. One important challenge in developing synapse devices for artificial neural networks lies in constructing these networks at a hardware level to achieve in-memory computing, enabling the efficient processing of information while minimizing power consumption. Herein, we present a rational design and in situ synthesis of two-dimensional (2D/2D) heteronanostructures intricately integrating Ti-based metal carbide as Ti-MXene (Ti3C2) with copper-based metal-organic framework as Cu-tetrakis (4-carboxyphenyl) porphyrin (Cu-TCPP) through van der Waals interactions to form a hybrid as [Ti3C2@Cu-TCPP] (1). The hybrid exhibits synergistic properties of both counterparts with an intricate hierarchical structure, ensuring exceptional stability and remarkable conductivity, fundamental for the progression of advanced neuromorphic devices. The resultant hybrids show an advanced neuromorphic device with comprehensive comparative analysis using DC I-V sweeps was conducted to evaluate different device types, focusing on parameters such as the high-resistance state, low-resistance state, and on/off ratio. Results demonstrated that Ti3C2@Cu-TCPP@PVA-based devices exhibited an impressive on/off ratio of approximately 10(2), outperforming Cu-TCPP@PVA and Ti3C2@PVA-based devices. This highlights the superior performance of Ti3C2@Cu-TCPP@PVA and its potential for advanced applications in neural network systems. Furthermore, the conduction mechanism was elucidated, revealing the dominance of the space-charge limited conduction mechanism during the SET process and the Schottky emission mechanism during the RESET process.

This review emphasizes the crucial role of chemiresistive gas sensors (CGS) in gas detection. It underscores porous materials as alternatives, showcasing their exceptional attributes. The review explores CGS-based porous materials in real-life applications.

This letter presents a precision low-noise sensor readout system with a bipolar junction transistor (BJT)-input-based amplifier and a high-input-impedance delta-sigma analog-to-digital convertor (ADC). The proposed sensor readout system can precisely drive the resistive sensors and attain high accuracy by adopting the low-noise BJT-input-based amplifier implementing gated p-n-p and the high-input-impedance delta-sigma ADC. The high-input-impedance delta-sigma ADC is implemented with a fully differential ping-pong autozeroing multipath differential difference amplifier (DDA), which can achieve high input impedance and low power consumption due to the DDA scheme that does not require input buffers. The proposed sensor readout system is fabricated in the standard 0.18-mu m complementary metal-oxide-semiconductor process with a total active area of 3.75 mm(2). The total current consumption of the sensor readout system is 543.9 mu A with 1.8-V power supply. The proposed sensor readout system has an ultralow-noise performance of the measured input offset and input referred noise of 1.7 mu V and 5.3 nV/root Hz, respectively. The measured signal-to-noise-and-distortion ratio in a 1-kHz bandwidth is 76.4 dB.

Metal-organic frameworks (MOFs) are promising materials for memristive synaptic devices due to their adaptable electrical properties and inherent porosity, which facilitate efficient ion transport and functional molecule storage. Here, we report MOFs from metabolites using ZnO as a self-sacrificial metallic source to grow zeolitic imidazolate framework-8 (ZIF-8), creating ZnO@ZIF-8 heteronanostructures for neuromorphic applications. These resultant synaptic devices exhibit superior electrical performance compared to ZnO single-layer devices, as demonstrated by current-voltage curve analysis and long-term potentiation/long-term depression (LTP/LTD) measurements. The bilayer devices achieve significantly improved endurance, reaching 200 cycles, and have a lower average set/reset voltage of 1.48/−0.59 V, indicating reduced power consumption. They also show an on/off ratio (HRS/LRS) of 44.54, with normalized SDs of 0.69, and nonlinearity in LTP and LTD of 0.58% and 0.30%, respectively, highlighting the ZIF-8 layer’s crucial role in enhancing neuromorphic system performance and reliability.

The effects of thermal annealing on analog resistive switching behavior in bilayer HfO2/ZnO synaptic devices were investigated. The annealed active ZnO layer between the top Pd electrode and the HfO2 layer exhibited electroforming-free resistive switching. In particular, the switching uniformity, stability, and reliability of the synaptic devices were dramatically improved via thermal annealing at 600 degrees C atomic force microscopy and X-ray diffraction analyses revealed that active ZnO films demonstrated increased grain size upon annealing from 400 degrees C to 700 degrees C, whereas the ZnO film thickness and the annealing of the HfO2 layer in bilayer HfO2/ZnO synaptic devices did not profoundly affect the analog switching behavior. The optimized thermal annealing at 600 degrees C in bilayer HfO2/ZnO synaptic devices dramatically improved the nonlinearity of long-term potentiation/depression properties, the relative coefficient of variation of the asymmetry distribution sigma/mu, and the asymmetry ratio, which approached 1. The results offer valuable insights into the implementation of highly robust synaptic devices in neural networks.

Two-dimensional (2D) nanomaterials have shown unique electrical and chemical characteristics essential for the enhancement of electronic and optoelectronic devices. Large-area fabrication of 2D materials like MoS2 is challenging due to poor nucleation and vertical multilayer growth. In this study, we employed wafer-scale CVD with gaseous precursors for MoS2 to facilitate large-area deposition and utilized NaCl to assist nucleation control. Precise control of the nucleation with NaCl has significantly increased the grain size of MoS2 from about 1-2 nm to 300 nm. However, a phase shift in MoS2 thin films from semiconductive 2H to metallic 1T phase was observed due to the Na intercalation. As a result, a shift in lower binding energy occurs in XPS analysis. Our findings contribute to the comprehension of phase transition phenomena in MoS2 thin films and further advancement in the synthesis techniques of MoS2 thin films within wafer-scale deposition environments.

Metal-organic frameworks (MOFs) are promising materials for memristive synaptic devices due to their adaptable electrical properties and inherent porosity, which facilitate efficient ion transport and functional molecule storage. Here, we report MOFs from metabolites using ZnO as a self-sacrificial metallic source to grow zeolitic imidazolate framework-8 (ZIF-8), creating ZnO@ZIF-8 heteronanostructures for neuromorphic applications. These resultant synaptic devices exhibit superior electrical performance compared to ZnO single-layer devices, as demonstrated by current-voltage curve analysis and long-term potentiation/long-term depression (LTP/LTD) measurements. The bilayer devices achieve significantly improved endurance, reaching 200 cycles, and have a lower average set/reset voltage of 1.48/-0.59- 0.59 V, indicating reduced power consumption. They also show an on/off ratio (HRS/LRS) of 44.54, with normalized SDs of 0.69, and nonlinearity in LTP and LTD of 0.58% and 0.30%, respectively, highlighting the ZIF-8 layer's crucial role in enhancing neuromorphic system performance and reliability.

In this study, the bipolar switching behaviors in ZnO/HfO2 bilayer resistive random-access memory (RRAM), depending on different metal top electrodes (TE), are analyzed. For this purpose, devices with two types of TE–TiN/Ti and Pd, which have varying oxygen affinities, are fabricated. X-ray diffraction (XRD) analysis shows that ZnO has a hexagonal wurtzite structure, and HfO2 exhibits both monoclinic and orthorhombic phases. The average grain sizes are 10.9 nm for ZnO and 1.55 nm for HfO2. In regards to the electrical characteristics, the I–V curve, cycling test, and voltage stress are measured. The measurement results indicate that devices with TiN/Ti TE exhibit lower set and higher reset voltage and stable bipolar switching behavior. However, a device with Pd TE demonstrates higher set and lower reset voltage. This phenomenon can be explained by the Gibbs free energy of formation (∆Gf°). Additionally, the Pd TE device shows unstable bipolar switching characteristics, where unipolar switching occurs simultaneously during the cycling test. This instability in devices with Pd TE could potentially lead to soft errors in operation. For guaranteeing stable bipolar switching, the oxygen affinity of material for TE should be considered in regards to ZnO/HfO2 bilayer RRAM.

Zinc tungsten oxide (ZW) and colloidal SnO2 quantum dots (CS) were synthesized individually by hydrothermal and wet chemical methods. ZW-CS core@shell nanorods were prepared using a sonochemical method for the enhanced photocatalytic activity of tetracycline (TC) degradation. ZW-CS core@shell nanorods were systematically characterized by structural, morphological mapping and optical techniques. All characterization techniques were synchronized to confirm the construction of core@shell nanorods. Optical absorption studies indicate an increased light-capturing efficiency along with a reduced bandgap from 3.56 to 3.23 eV, which is further supported by photoluminescence. Mapping analysis from SEM and HR-TEM evidence the presence of elements as well as a core@shell nanostructure. The optimized sample of ZW-CS 1.0 shows improved photocatalytic degradation of TC under stimulated solar light. The TC degradation efficiency by ZW-CS 1.0 core@shell nanorods was about 97% within 2 h. The formation of core@shell nanorod structure might be the reason for the better photocatalytic tetracycline degradation performance.

This study investigates the temperature coefficient of resistance (TCR) of a-Si:H according to process conditions to find out the temperature-dependent mechanism for microbolometer applications. Four types of a-Si:H films were prepared using plasma-enhanced chemical vapor deposition (PECVD) by adjusting the SCCM of the doping gases: high B2H6 (B1), low B2H6 (B2), high PH3 (P1), and low PH3 (P2). Secondary ion mass spectroscopy analysis is performed to confirm the doping concentration. N-type (P1 or P2) seems to be less favorable for improving the channel conductance, but it has a large TCR meaning that P2 has the highest temperature dependency as well as the largest absolute TCR value. After preannealing in D-2 atmospheres, P1 and P2 show a significant reduction in the TCR. In addition, RC is observed to be reduced with TCR after postmetal annealing. The 1/f noise is also correlated with TCR and is found to have a tradeoff relation. These results imply that the TCR is primarily influenced by the trap states in a-Si:H film. Based on the experimental results, thermal-assisted transport through the local potential barrier is suggested for the temperature dependency of TCR in a-Si:H.

In this paper, an auto-store circuit that can perform a store and restore operation of non-volatile SRAM (nvSRAM) with access transistor whose gate stack is Silicon-Oxide-Nitride-Oxide-Silicon (SONOS) is designed. The suggested auto-store circuit is consisted by two small circuit parts of a store and restore circuit. The store circuit moves data in SRAM cell into SONOS device before power turns off while a restore circuit recalls the data to SRAM cell when the power is resupplied. When the nvSRAM replaces its conventional access transistor with the SONOS flash memory device, higher word line voltage is needed for a store operation. It demands much energy and additional voltage source to store a data. To overcome this disadvantage, the capacitor component is added in the store and restore circuit which is charged during the normal operation mode. At the moment of the poweroff, the store circuit discharges the capacitors and applies higher voltage to the word line for the store operation. When the power is supplied again, the restore circuit discharges the capacitor and applies pre-charge voltage and restore the data to the bit line and word line. The simulation results show that the suggested auto store circuit can perform the store, restore operation in the sudden power off without an additional voltage source.