With the advancement of Internet of Underwater Things (IoUT), underwater intelligent vehicles are becoming more compact, clustered, and diverse, leading to a significant rise in the demand for underwater communication. Rely on the minimal attenuation of acoustic signals underwater and the long transmission distances achievable, acoustic communication has garnered significant attention. However, the lack of thorough investigation into the correlation between device characteristics, such as bandwidth, signal-to-noise ratio (SNR), and material properties, hinders the development of high-speed and accurate data transmission underwater. In this study, we designed and fabricated the X-cut single-crystal lithium niobate (LiNbO(3 )or LN) piezoelectric micromachined ultrasonic transducer (PMUT) with a lateral-field-excitation (LFE) structure. The investigation focused on the correlation between piezoelectric properties of materials and device performance under different in-plane orientations. Underwater experimental results demonstrate that the LN PMUTs achieve a wide bandwidth of 2.31 MHz (70.1% at -6 dB) and possess a high SNR of 81 dB. Furthermore, the LN PMUTs efficiently detect signals encoded in the 2-amplitude shift keying (2ASK) protocol at a transmission rate of 650 kbit/s at 1.25 m. These findings indicate that LN PMUTs, with their high bandwidth and SNR, can provide technical support for efficient communication in underwater electronic devices.

Artificial sensory memory is a novel way to solve the contradiction between the information explosion in the era of the Internet of Things (IoT) and the high demand for hardware resources by artificial intelligence, which achieves the integration of sensory and memory by mimicking biological neural system. Here, we innovatively propose an artificial infrared neural system (AINS) based on single-crystal thin films, consisting of artificial receptors, artificial afferent fibers, and artificial synapses. It is implemented by a pyroelectric sensor based on lithium tantalate (LT) thin film, a threshold-based signal processing module, and a memristor based on lithium niobate (LN). After demonstrating the detection ability of the pyroelectric sensor and the short-term plasticity (STP) and time coding ability of the memristor, we successfully coupled them together to achieve the conversion of pyroelectric current (PEC) signal to postsynaptic current (PSC). Based on the above characteristics, the output of an AINS-based visual array for multiple dynamic hand-waving action was further simulated. The results showed that our AINS can achieve intra-recognition of historical events via spatiotemporal fusion imaging. The proposed AINS realizes sensing, memorizing, and processing of sensor information in the analog domain, opening a novel avenue for sensor signal processing.

A surface acoustic wave (SAW) filter’s bandwidth and quality are determined by its resonators’ electromechanical coupling coefficient ( k 2 ) and impedance ratio (IR). Research commonly focuses on the effects of piezoelectric material and cutting direction on these parameters. This paper investigates the effect of the conductivity of the Si substrate on k 2 and IR through finite element method (FEM) simulations. A new model based on the modified Butterworth-van-Dyke (MBVD) model is presented. This new model takes into account the substrate parasitic capacitance and resistance to predict resonator performance on low resistivity (LR) Si piezoelectric on insulator (POI) substrates. Both high resistivity (HR) Si and LR-Si are utilized to fabricate POI SAW resonators, which are subsequently tested. The high conductivity of the Si support layer leads to a decrease in both k 2 and IR. By employing Si substrates with different resistances during fabrication, it becomes possible to manufacture resonators with varying k 2 values, thus meeting diverse bandwidth requirements for filters.

Flexible surface acoustic wave (SAW) resonators on single crystal LiNbO3(LN) film have been successfully fabricated using benzocyclobutene bonding and crystal ion slicing technology. The effects of Polyimide (PI) thickness on the properties of LN film have been studied. 960 nm LN single crystal films without cracks have been transferred to $25 \mu \mathrm{~m}$ PI substrates, which are used to construct flexible SAW resonators. The flexible SAW resonators show well frequency-strain response characteristics. These results demonstrated that LN single crystal film on PI substrate and its SAW resonator is a promising candidate in flexible electronic devices.

To ensure that surface acoustic wave (SAW) filters fulfill the requirements of Carrier Aggregation (CA) applications, the development of modeling tools that can forecast and simulate high-frequency spurious responses has been necessary. This paper presents an advanced methodology for extending the coupling-of-modes (COM) model to obtain precise modeling of the high-frequency spurious responses of incredible high-performance surface acoustic wave (I.H.P. SAW) devices. The extended COM (ECOM) model is derived by modifying the conventional COM model and extending it accordingly. The parameters used in this model are determined through numerical fitting. For validation, firstly, the ECOM model is applied to a one-port synchronous I.H.P. SAW resonator, and the simulation and measurement results match. Then, the structural parameters of the ECOM model are varied, and the accuracy of the model after the structural parameters are varied is verified. It is demonstrated that this model can be applied to the design work of SAW filters. Finally, the ECOM model is applied to the design of the I.H.P. SAW filter based on a 42°YX-LiTaO3 (LT)/SiO2/AlN/Si structure. By using this method, the I.H.P. SAW filter’s high-frequency spurious response can be predicted more accurately.

The implementation of multilevel conductance states is still difficult for passive memristors used in neuromorphic computing. Here, a passive single-crystalline LiNbO3 (LN) memristor with multilevel states was proposed, which can be precisely programmed into multilevel target states (with a standard deviation below 0.008 mu s). Moreover, 32 separated and reliable conductance states can be achieved. The pattern recognition simulation for different numbers of conductance states (N-G) is performed. As N-G increases, the inference accuracy rises and reaches 98.01% when N-G is 32. Even taking into account the conductance programming and drift error of the memristors, the accuracy can still reach 90.37%. The results validate the application potential of this passive memristor with 32 conductance states in neuromorphic computing.

Memristors are recognized as crucial devices for the hardware implementation of neuromorphic computing. The conductance training process of memristors has a direct impact on the performance of neuromorphic computing. However, memristor breakdown and conductance decay still hinder the precise training process of neural networks based on passive memristor. Here, AlOx/LiNbO3 (LN) memristors are designed by inserting a AlOx sub-oxide layer between the single-crystalline LN thin film with oxygen vacancies (OVs) and Pt layer. Under the same training conditions, lower conductance and self-compliance current effects are observed in AlOx/LN memristor. Slight spontaneous decay of conductance is achieved after the removal of the external stimulation. To explore the effects of AlOx sub-oxide layer on the prevention of device breakdown and suppression of conductance decay, the memristive mechanism of devices with and without AlOx layer is revealed via time-of-flight secondary ion mass spectrometer (ToF-SIMS). It is reasonable to believe that the AlOx inserting layer in memristors can serve as a self-compliance current layer to inhibit device breakdown and provide the OVs reservoir to suppress conductance decay. These results offer new possibilities and theoretical grounds for achieving more reliable and precise conductance training of passive memristors.

The crystal ion slicing (CIS) fabricated heterogeneously integrated lithium tantalate (LiTaO3, LT) thin film is prospective in optical modulators, infrared sensors, and acoustic filters, therefore improving the transferring efficiency of the LT thin film will definitely promote its development and application. In the present work, the chemical reduction method was found effective to enhance the forming ability of H+ implantation-induced defects and surface blistering efficiency of lithium tantalate crystal during the CIS process. According to the investigations by XPS, XRD, RBS, Micro-Raman, and TEM on the reduction-induced defects, H+ implantation-induced defects, and the surface blistering behavior, the treated black lithium tantalate (BLT) present a higher blistering efficiency, confirming the contribution of reduction-induced defects in implantation and blister formation process than the referenced congruent lithium tantalate (CLT). In detail, the improvement is in ascribing to the existing intrinsic oxygen vacancy defects in BLT. These defects increase the forming efficiency of Ta defects in the implantation process and enhance the diffusion of H+ in the blistering formation process, thus decreasing the activation energy for the blistering process and increasing the film exfoliation area. These results highlight an avenue for enhancing ion slicing efficiency of piezoelectric and pyroelectric single crystal oxide.

This paper reports an LNO surface acoustic wave (SAW) resonator based on a shear horizontal mode with high operating frequency over 3 GHz, large electromechanical coupling of 33.54%, Q factor of 380, and a relatively good figure of merit (FOM) of 127. Combing crystal-ion-slicing (CIS) technology with a room temperature bonding method, a 4-inch single crystalline LNO thin film on silicon is prepared successfully. The influence of damaged LNO film on crystalline quality and SAW performance is comprehensively analyzed. After totally removing the damaged layer, the electromechanical coupling and Q factor is significantly improved. The high-performance SAW resonator possesses the potential to meet the requirements of SAW filters for the fifth-generation (5G) communication in terms of high frequency, large bandwidth, and a high-quality factor.

The crystal-ion-slicing technology is a promising technology for fabricating high-quality single crystal piezoelectric thin films. This work attempted to investigate the influence of annealing ambience on the properties of LiTaO3 thin films fabricated by crystal-ion-slicing technology. Here, two 4-inch 42° rotated Y-X propagation LiTaO3 thin films were fabricated, as well as the LiTaO3 thin films were annealed in vacuum and O2 environment, respectively. The results demonstrate that the LiTaO3 thin films annealed in O2 environment possesses more homogeneous and denser structure and fewer defects. The Zratio of the resonators fabricated on the LiTaO3 thin films annealed in O2 environment is approximately 85.3 dB and the Q-factor reaches 5107, indicating the excellent piezoelectric properties of thin films.

To efficiently process the massive amount of sensor data, it is demanding to develop a new paradigm. Inspired by neurobiological systems, an infrared near-senor reservoir computing (RC) system, consisting of infrared sensors and memristors based on single-crystalline LiTaO3 and LiNbO3 (LN) thin film respectively, is demonstrated. The analog memristor is used as a reservoir in the RC system to process sensor signals with spatiotemporal characteristics. LN crystal structure stacked with oxygen octahedra provides favorable conditions for reliable Mott variable-range hopping conduction, which provides the memristor with tens of thousands of reservoir states within a large dynamic range. With the characteristics, the analog sensor signals with high data fidelity can be directly fed to the memristive reservoir, and the spatiotemporal features can be separated and mapped. The system demonstrated a dynamic gesture perception task, achieving an accuracy of 99.6%, which highlights the great application potential of the memristor in signal sensor processing and will advance the application of artificial intelligence in sensor systems. Crystal ion slicing techniques are used to fabricate a single-crystalline thin film for both the memristor and sensor, which opens up the possibility of realizing monolithic integration of a memristor-based near-sensor computing system.

One of the key steps toward constructing neuromorphic systems is to develop reliable bio-realistic synaptic devices. Here, memristors based on single-crystalline LiNbO3 (SC-LNO) thin film are fabricated as artificial synapses. A reservoir of oxygen vacancies is induced by Ar+ irradiation to resemble synaptic vesicles containing neurotransmitters. Phenomena of saturation and adaptivity, short-term plasticity, paired-pulse facilitation, paired-pulse depression, and long-term potentiation are successfully mimicked. The dynamic transition from sensory memory to short-term memory, and further to long-term memory, is also successfully emulated for multipattern memorization. In addition, first, taking advantage of short- and long-term synaptic plasticity is proposed, to realize experience-based image mask generation with different stimuli schemes. During the experience-based generation process, memristive multi-value masks (MMVMs) are generated with different numbers of stimuli applied to the memristor at each pixel, which corresponds to the times the region occurred in the history image set. The experience-based memristive multi-value mask successfully extracts multiple regions of interest with different priorities. This work demonstrates that the memristor based on Ar+-irradiated SC-LNO thin film with bioinspired microstructure shows great potential in future neuromorphic systems for experience-based intelligent image processing.

With the development of artificial intelligent IoT (AIoT) and dramatic increase of the amount of data, conventional architecture, in which all the data collected by sensors are sent to the data center for processing and computing, suffered from heavy computing load in the data center, latency, and high energy consumption. Edge neural network computing at the sensor terminal is demanding. Based on the advantages of memristor in nature co-location of memory and computing, high computing parallelism, low energy consumption, and miniaturization potential, we proposed to take use of memristors to conduct edge neural network computing at sensor and realize the integration of sensing memory computing. Also, in order to solve the problems caused by resistance states variation and device-to-device variation without transistor connected in series, highly uniform memristors based on single-crystalline LiNbO3 (LN) thin film with two stable resistance states were fabricated and utilized to realize binarized neural networks computing, and the coupling between the pressure sensor output signal with the input of the memristor array has been built. The hardware implementation of memristor-based edge neural network computing on the signals of pressure sensor array has been realized. With the memristor-based edge neural network computing, recognition of three letters (“V,” “Z,” and “T”) wrote on the pressure sensor array has been realized.

Working memory refers to the brain's ability to store and manipulate information for a short period. It is disputably considered to rely on two mechanisms: sustained neuronal firing, and "activity-silent" working memory. To develop a highly biologically plausible neuromorphic computing system, it is anticipated to physically realize working memory that corresponds to both of these mechanisms. In this study, we propose a memristor-based neural network to realize the sustained neural firing and activity-silent working memory, which are reflected as dual functional states within memory. Memristor-based synapses and two types of artificial neurons are designed for the Winner-Takes-All learning rule. During the cognitive task, state transformation between the "focused" state and the "unfocused" state of working memory is demonstrated. This work paves the way for further emulating the complex working memory functions with distinct neural activities in our brains.

A research was conducted on the performance of a resonator with a piston structure at the end of the interdigital electrode, on an incredible high-performance surface acoustic wave (I.H.P. SAW) substrate. Based on this substrate structure, the corresponding three-dimension finite element simulation model was established. Fixing the piston width is 0.325P (P is period), through simulation and taped-out, it was concluded that a piston length of 0.8P at a period of $2.0 \mu \mathrm{m}$, the impedance ratio of the resonator is the highest, and the transverse mode is also well suppressed. The above simulation and measurement results demonstrate that the length of the piston have a significant impact on the performance of the I.H.P. SAW resonator, providing design guidance for high-performance resonators applied in I.H.P. SAW filters.

This paper studied the impact of the microstructure of interdigital electrodes on the performance of surface acoustic wave (SAW) resonators and proposed an innovative piston, dummy finger and tilt (PDT) structure, which was then applied to the GLONASS L3 band filters. Through the adoption of 3D finite element simulation (FEM), photolithography, and testing on an incredible high-performance surface acoustic wave (I.H.P. SAW) substrate, it is concluded that the total aperture length is 20T (T is period), resulting in a more optimal resonator performance; changing the width and length of the piston can suppress transverse modes spurious, but it does not enhance impedance ratio; to further improve the quality of the SAW resonator, the proposed PDT structure has been experimentally proven to not only effectively suppress transverse modes spurious but also possess a high impedance ratio. By utilizing a PDT structure within a "T + π" topology circuit, we successfully designed and manufactured a GLONASS L3 band filter with a bandwidth of 8 MHz and an insertion loss of 3.73 dB. The design of these resonators and filters can be applied to the construction of SAW filters in similar frequency bands such as BeiDou B2 band or GPS L2/L5 band.

Inspired by human brain, the emerging analog‐type memristor employed in neuromorphic computing systems has attracted tremendous interest. However, existing analog memristors are still far from accurate tuning of multiple conductance states, which are crucial from the device‐level view. Herein, a reliable analog memristor based on ion‐slicing single‐crystalline LiNbO3 (LNO) thin film is demonstrated. The highly ordered LNO crystal structure provides a stable pathway of oxygen vacancy migration, which is contributed to a stable Mott variable‐range hopping process in trap sites. Excellent analog switching characteristics with high reliability and repeatability, including long retention/great endurance with small fluctuation (fluctuated within 0.22%), a large dynamic range of two orders of magnitude, hundreds of distinguishable conductance states with tunable linearity, and ultralow cyclic variances for multiple weight updating (down to 0.75%), are realized with the proposed memristor. As a result, a multilayer perceptron with a high recognition accuracy of 95.6% for Modified National Institute of Standards and Technology dataset is realized. The proposed analog memristive devices based on ion‐slicing single‐crystalline thin films offer a novel strategy for fabricating high‐performance memristors that combined linear tunability and long‐term repeatability, opening a novel avenue for neuromorphic computing application.

To achieve the goal of neuromorphic computing hardware implementation with extremely high efficiency, low power consumption, and high density, it is necessary to develop transistor-free memristors and implement differential operation without subtraction circuits. In this study, argon ion irradiation was used during the fabrication process of a single crystalline LiNbO3 (LN) thin film to controllably introduce oxygen vacancies (OVs) into the bottom surface, which realized the modulation of OVs based on the excellent environment provided by a single-crystalline thin film. The memristive behavior of memristors was then modulated by regulating the distribution of OVs, and the effect of OVs distributed near the bottom surface of the single crystalline LN thin film on the memristive behavior was analyzed. In this way, two transistor-free memristors with opposite memristive behavior directions were fabricated. Two transistor-free memristors exhibit excellent synaptic plasticity and reliable multilevel resistance states. Based on two transistor-free memristors, a novel differential pair was constructed. Hardware implementations of direct differential operation without subtraction circuits were achieved. This study provides a new pathway to develop a transistor-free memristor and achieve differential operation without subtraction circuits in neuromorphic computing, which will simplify the peripheral circuits, improve integration density, and reduce power consumption and latency.

Negative differential resistance (NDR) in memristor has attracted considerable attention due to its nonlinear dynamic characteristic accompanied by resistive switching behavior. Here, we demonstrated the voltage-programmable NDR effect in an Au/LNO/Pt memristor based on a single-crystalline lithium niobate (SC-LNO) thin film tuned by low-energy Ar+ irradiation. A typical N-Shape NDR behavior occurs in reverse bias and can be programmed by changing the positive sweeping voltage. Moreover, thickness-dependent of the NDR behavior was also investigated. Thanks to the merit of the SC-LNO thin film, our memristor reveals excellent uniformity and reproducibility with low cycle-to-cycle variation (down to 1.82% for Ipeak and 2.94% for Vvalley). The carrier transport mechanism of our device is attributed to the interplay of trap-controlled space-charge-limited conduction (SCLC) and the Fowler–Nordheim (FN) tunneling. The transition from bulk-limited conduction (SCLC) to interfacial one (FN) tuned by Ar+ irradiation is crucial for our NDR devices.

Typical filament-type memristors suffer from temporal and spatial variations in the resistive switching due to stochastic filament formation, which hinders the implementation of memristive synapses in neuromorphic computing. In this work, the memristor based on Ar+-irradiated single-crystalline LiNbO3 (SC-LNO) thin film is reported. The high-quality SC-LNO thin films contribute to the formation of more concentrated and robust oxygen vacancy channels (OVCs) and lead to reliable self-rectifying resistive switching behaviors with an ultra-low device-to-device variability of -2.80% and cycle-to-cycle variability of -2.25%, as well as long retention time at multilevel resistance states (>3 x 104 s), stable endurance performance (>105 cycles), excellent environmental stability (>6 months), and good analog switching linearity. Furthermore, abundant synaptic plasticity characteristics including paired-pulse facilitation (PPF), post-tetanic potentiation (PTP), long-term potentiation (LTP), long-term depression (LTD), spike-timing-dependent plasticity (STDP), and associative learning, are also successfully emulated using this device as an electronic synapse. The spontaneous decay process right after applied voltage pulses and the long-term nonvolatility characteristics in Ar+-irradiated SC-LNO memristor indicate the existence of residual OVCs, which serves to guide the reconstruction of OVCs during repeated switching and to further enhances the uniformity of device switching characteristics.