Neuromorphic devices have garnered significant attention for their potential to revolutionize conventional computing architecture and drive advancements in artificial neural systems. Aluminum nitride-based (AlN-based) memristors have emerged as particularly noteworthy due to their exceptional properties, including ultrafast switching speed, small switching current, substantial on/off ratio, controllable material growth, and compatibility with complementary metal-oxide-semiconductor (CMOS) processes. These remarkable characteristics hold immense significance in the fabrication of novel neuromorphic devices, specifically for artificial synapses. However, the commonly observed abrupt resistive switching behavior in AlN-based memristors poses a challenge to the recognition accuracy of artificial neural networks (ANNs) at the system level. Thus, achieving a gradual switching behavior with multi-level conductance becomes highly desirable for artificial synapses. Here, an interfacial engineering approach is introduced to optimize the Ag/AlN/Pt memristor by incorporating an aluminum scandium nitride (AlScN) layer within the AlN layer. The tri-layer AlN/AlScN/AlN stacked memristor (ASAM) demonstrates a notable achievement of gradual switching behavior at the RESET operation, attributed to the alleviation of abrupt conductive filament formation resulting from the ferroelectric polarization effect of the AlScN layer. Additionally, the ASAM shows excellent resistive switching performance with ultrafast switching speed (<5 ns), low operation voltage (<0.5 V), and ultralow power consumption as small as 0.2 pJ. By appropriately adjusting the current compliance and resetting stop voltage, the ASAM exhibits remarkable characteristics of controlled gradual switching and multi-level conductance. Moreover, the ASAM successfully mimics biological synaptic functions, such as long-term potentiation (LTP), long-term depression (LTD), paired-pulse facilitation (PPF), and Spike-Timing-Dependent Plasticity (STDP). Leveraging the near linearity of conductance modulation provided by the ASAM, the MNIST handwritten digits recognition task, which is simulated using experimental data by constructing a convolutional neural network (CNN), can achieve a high accuracy of 93% after 150 epochs. Importantly, the design scheme of the stacked device structure with polarization effect holds significant promise for the fabrication of nitride-based memristors with highly linear and symmetrical conductance regulation. Such characteristics are crucial for the development of analog computing architecture, enabling more efficient and accurate neuromorphic systems. 

Artificial synapse based on a tri-layer AlN/AlScN/AlN stacked memristor for neuromorphic computing

Bonding diamond to the back side of gallium nitride (GaN) electronics has been shown to improve thermal management in lateral devices; however, engineering challenges remain with the bonding process and characterizing the bond quality for vertical device architectures. Here, integration of these two materials is achieved by room-temperature compression bonding centimeter-scale GaN and a diamond die via an intermetallic bonding layer of Ti/Au. Recent attempts at GaN/diamond bonding have utilized a modified surface activation bonding (SAB) method, which requires Ar fast atom bombardment immediately followed by bonding within the same tool under ultrahigh vacuum (UHV) conditions. The method presented here does not require a dedicated SAB tool yet still achieves bonding via a room-temperature metal-metal compression process. Imaging of the buried interface and the total bonding area is achieved via transmission electron microscopy (TEM) and confocal acoustic scanning microscopy (C-SAM), respectively. The thermal transport quality of the bond is extracted from spatially resolved frequency-domain thermoreflectance (FDTR) with the bonded areas boasting a thermal boundary conductance of >100 MW/m2·K. Additionally, Raman maps of GaN near the GaN-diamond interface reveal a low level of compressive stress, <80 MPa, in well-bonded regions. FDTR and Raman were coutilized to map these buried interfaces and revealed some poor thermally bonded areas bordered by high-stress regions, highlighting the importance of spatial sampling for a complete picture of bond quality. Overall, this work demonstrates a novel method for thermal management in vertical GaN devices that maintains low intrinsic stresses while boasting high thermal boundary conductances.

Thermal Transport and Mechanical Stress Mapping of a Compression Bonded GaN/Diamond Interface for Vertical Power Devices.

In artificial synaptic devices aimed at mimicking neuromorphic computing systems, electrical or optical pulses, or both, are generally used as stimuli. In this work, we introduce chiral materials for tailoring the characteristics of photonic synaptic devices to achieve handedness-dependent neuromorphic computing and in-memory logic gates. In devices based on a pair of chiral perovskites, the use of circularly polarized light (CPL) as the optical stimuli mimicked a series of electrical and opto-synaptic functionalities in order to emulate the multifunctional complex behavior of the human brain. Upon illumination in this two-terminal device, anisotropy in current has been observed due to the out-of-plane carrier transport, originating from spin-selective carrier transport. More importantly, the logic gate achieved in devices based on optoelectronic memristors turned out to be chirality-dependent; while an R-device functioned as an AND gate, the device based on the same perovskite of the opposite chirality (S-device) acted as a NOR gate toward in-memory logic operations. These findings in chiral perovskite-based artificial synapses can identify further strategies for future neuromorphic computing, vision simulation, and artificial intelligence.

Introducing Chiro-optical Activities in Photonic Synapses for Neuromorphic Computing and In-Memory Logic Operations.

In the realm of modern wireless networks, the integration of wireless sensing and communication systems is pivotal, especially in the context of the forthcoming 6G Internet of Things (IoT) paradigm. The popularity of integrated sensing and communication (ISAC) stems from its potential to amplify the utilization of existing network infrastructures. This study introduces an innovative fusion of joint communication and sensing (JCAS), a framework of ISAC, combined with end-to-end (E2E) network slicing (NS) techniques. The aim is to meet user Quality-of-Service (QoS) expectations within the ambit of 6G IoT applications. The prime objective is to optimize resource allocation for communication and sensing services within a bespoke network slice tailored for 6G IoT scenarios. This is achieved by minimizing E2E system latency, essential for real-time decision making in 6G IoT environments. The optimization challenge is tackled by using deep reinforcement learning (DRL) in the form of a deep $Q$ network (DQN) algorithm, which is adept at addressing nonlinear integer programming (NLIP) issues intrinsic to 6G IoT settings. Comprehensive simulations validate the approach, demonstrating its effectiveness in the context of 6G IoT networks. The amalgamation of ISAC with E2E NS emerges as a potent strategy for furnishing enhanced services customized for 6G IoT applications, successfully fulfilling consumers’ QoS requisites. This integrated approach holds substantial promise as a robust solution for addressing the exacting demands of forthcoming wireless networks, particularly those underpinned by the strides in 6G IoT technologies.

AI-assisted E2E Network Slicing for Integrated Sensing and Communication in 6G Networks

Conjugated polymers exhibit significant potential for use in gas sensors owing to their flexibility and straightforward preparation. However, the performance of organic gas sensors based on such materials is currently suboptimal owing to their poor charge‐transport properties and limited selectivity. To address this limitation, a novel strategy that involves blending a chemically modified metal–organic framework (MOF) material, UiO‐66, with a conjugated polymer is developed. The MOF porous material is chemically modified with polar nitrogen‐containing functional groups (amino and nitro groups) to create gas molecule adsorption sites. To evaluate the impact of these modifications on the sensing performance, experiments that combined the sensing performance with a density functional theory simulation are conducted. The findings reveal that gas sensors fabricated using an amine‐functionalized MOF blended with conjugated polymers exhibit significantly higher sensitivity than home polymer devices, the sensing performance of the UiO‐66‐NH2/P3HT blend film improved by a factor of two with a sensitivity of ≈2%/ppm and an LOD of 0.001 ppt. Additionally, these devices exhibit exclusive NO2‐sensing performance for other gases such as CO2 and SO2.

Enhanced Gas Sensing Characteristics of a Polythiophene Gas Sensor Blended with UiO‐66 via Ligand Functionalization

The memristor, characterized by its nano-size, nonvolatility, and continuously adjustable resistance, is a promising candidate for constructing brain-inspired computing. It operates based on ion migration, enabling it to store and retrieve electrical charges. This paper reviews current research on synapses using digital and analog memristors. Synapses based on digital memristors have been utilized to construct positive, zero, and negative weights for artificial neural networks, while synapses based on analog memristors have demonstrated their ability to simulate the essential functions of neural synapses, such as short-term memory (STM), long-term memory (LTM), spike-timing-dependent plasticity (STDP), spike-rate-dependent plasticity (SRDP), and paired-pulse facilitation (PPF). Furthermore, synapses based on analog memristors have shown potential for performing advanced functions such as experiential learning, associative learning, and nonassociative learning. Finally, we highlight some challenges of building large-scale artificial neural networks using memristors. 

Research Progress of Neural Synapses Based on Memristors

In this letter, we analyze the performance trade-off in distributed integrated sensing and communication (ISAC) networks. Specifically, with the aid of stochastic geometry theory, we derive the probability of detection of that of the coverage given user number. Based on the analytical derivations, we provide a quantitative description of the performance limits and the performance trade-off between sensing and communication in a distributed ISAC network under the given transmit power and bandwidth budget. Extensive simulations are conducted and the numerical results validate the accuracy of our derivations.

On the Performance Trade-Off of Distributed Integrated Sensing and Communication Networks

Compared with electrons, photons have the potential to realise ultra-high speed operations because of its unique high speed and high parallelism. In recent years, there have been many researches on neural networks using optical hardware. The Mach–Zehnder interferometer (MZI) and micro-ring resonator (MRR) are commonly used as optical devices to realise linear operation units in optical neural networks (ONN). MZI has the advantages of simple fabrication, high sensitivity, and easy integration, which has attracted the attention of researchers. We summarise the implementation methods of ONN matrix multiplication based on MZI, the implementation methods of non-linear activation, and the on-chip training methods. We first summarise the researches on matrix multiplication of ONN based on MZI. Three kinds of MZI grid decomposition methods, Fast Fourier Transform (FFT) grid structures, and the corresponding derivation processes are introduced, respectively. Then, several experimental implementations of ONN based on MZI are summarised, and the characteristics of optical processors fabricated in these references are analysed. Finally, the realisation methods of non-linear activation and on-chip training of silicon ONN are introduced, respectively. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ote2.12086

Implementation of optical neural network based on Mach–Zehnder interferometer array

The adjustable bandgap, single crystal structure, and strong chemical inertness of GaN materials make them excellent candidates for water splitting applications. The fabrication of GaN nanostructures can enhance their water splitting performance by increasing their surface area, improving photon absorption, and accelerating photocatalytic reactions. Developing cost-effective methods to fabricate GaN nanostructures is crucial to promote the development of GaN-based materials in water splitting applications. In this review, we introduce the main cost-effective techniques for the fabrication of GaN nanostructures and highlight future development directions.

/pdf/the-fabrication-of-gan-nanostructures-using-cost-effective-dorta45u.pdf

The Fabrication of GaN Nanostructures Using Cost-Effective Methods for Application in Water Splitting

After the discovery of graphene in 2004, two dimensional (2D) materials have fascinated a lot of view due to the excellent properties. Nowadays, the research on 2D materials has spread to other graphene-like layer structured materials, especially transition metal dichalcogenides (TMDCs). Tin disulfide (SnS2) is a kind of TMDCs with a sizable bandgap. Here we introduce few-layer SnS2 field-effect transistors (FETs) fabricated using micromechanical exfoliation method. The FETs show n-type behavior, the on/off ratio exceeding 0.54×104 and the carrier mobility is 0.61 cm2V−1s−1. The electronic and optical characteristics of SnS2 flakes with a finite bandgap illustrate their potential applications in optoelectronics device.

Electrical Transport Properties of Few-Layer SnS2 Field-effect Transistors

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