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Person re-identification (ReID) is widely used in intelligent security, monitoring, criminal investigation and other fields. Aiming at the problems of local occlusion, scale misalignment and attitude change of pedestrian images in actual scenes, we propose a Multi-local Feature and Attention fused network (MFA) used for person re-identification task. Firstly, Channel Point Affinity Attention module (CPAA) is embedded in the backbone network to enhance the ability of the network for extracting local details. Then, the feature map output from the backbone network is horizontally segmented into four local feature maps, and further four branch networks are concatenated to the feature map of the backbone network. The four local feature maps are used to guide the four branch networks to pay more attention on different areas of pedestrians through Global Local Aligned loss (GLA) function. Finally, the pedestrian feature vector containing multi-local features is obtained. The mAP of the network on Market-1501 and DukeMTMC-reID datasets were 88.6% and 81.4%, and the Rank-1 is 95.8% and 90.1%, respectively. In addition, the model also obtained 73.2% and 68.1% of Rank-1 on partial dataset Patial-REID and Patial-iLIDS, respectively. Compared with other ReID methods, our proposed methods achieved a competitive performance for ReID task.The code was available at github:git@github.com:ISCLab-Bistu/MFA.git.

The transformation from silent to functional synapses is accompanied by the evolutionary process of human brain development and is essential to hardware implementation of the evolutionary artificial neural network but remains a challenge for mimicking silent to functional synapse activation. Here, we developed a simple approach to successfully realize activation of silent to functional synapses by controlled sulfurization of chemical vapor deposition-grown indium selenide crystals. The underlying mechanism is attributed to the migration of sulfur anions introduced by sulfurization. One of our most important findings is that the functional synaptic behaviors can be modulated by the degree of sulfurization and temperature. In addition, the essential synaptic behaviors including potentiation/depression, paired-pulse facilitation, and spike-rate-dependent plasticity are successfully implemented in the partially sulfurized functional synaptic device. The developed simple approach of introducing sulfur anions in layered selenide opens an effective new avenue to realize activation of silent synapses for application in evolutionary artificial neural networks.

In emerging Spiking Neural Network (SNN) based neuromorphic hardware design, energy efficiency and on-line learning are attractive advantages mainly contributed by bio-inspired local learning with nonlinear dynamics and at the cost of associated hardware complexity. This paper presents a novel SNN design employing fast COordinate Rotation DIgital Computer (CORDIC) algorithm to achieve fast spike timing–dependent plasticity (STDP) learning with high hardware efficiency. In this study, a system design and evaluation method of CORDIC-based SNN is proposed for finding optimal CORDIC type and precision, from theoretical CORDIC-level error to application-level learning performance. From the proposed design and evaluation method, a reconfigurable SNN design based on fast-convergence CORDIC is designed to achieve high classification accuracy on MNIST, fast on-line learning and good energy efficiency. By utilizing SNN’s fault tolerance and time-division-multiplexing (TDM) strategy, the reconfigurable SNN design employs 8-bit fast-convergence CORDIC and TDM-based hardware accelerator for high efficiency. FPGA implementation results confirm that the proposed fast-convergence CORDIC SNN design outperforms the state-of-the-art CORDIC method by 38.5%−45.3% in terms of learning speed and energy efficiency, with the STDP learning of 30.2 ns/SOP, energy efficiency of 176.6 pJ/SOP, processing speed of 6.1 ms/image, and on-line learning convergence of 21.4 s (time to reach the final accuracy, on average), on MNIST benchmark.

Thin-film architectures are a staple in a wide range of technologies, such as semiconductor devices, optical coatings, magnetic recording, solar cells and batteries. Despite the industrial success of thin-film technology, mostly due to the easy fabrication and low cost, a fundamental drawback remains: it is challenging to alter the features of the film once fabricated. Here we report a methodology to modify the thickness and sequence of the innermost solid-state thin-film layers. We start with a thin-film stack of amorphous iron oxide and silver. By applying a suitable voltage bias and then reversing it, we can float the silver layer above or below the oxide layer by virtue of the migration of silver atoms. Scanning transmission electron microscopy reveals various sequences and thicknesses of the silver and oxide layers achieved with different experimental conditions. As a proof-of-principle, we show a dynamic change of structural colours of the stack derived from this process. Our results may offer opportunities to dynamically reconfigure thin-film-based functional nanodevices in situ.

Memristive devices based on two-dimensional (2D) semiconducting materials have emerged as highly promising neuromorphic devices due to their intrinsic atomic body and unique properties. However, th...

In this work, we proposed a thin-film selector with a vacuum gap structure for neuromorphic application, which demonstrates outstanding performance including ultra-low leakage current (~0.20 pA), high ON/OFF ratio (> 109), record-high turn-on slope ( 108) and low turn-on energy (113.37 pJ). Our selector could minimize sneak currents in crossbar array, enabling terabits scale up capability. Moreover, we have integrated the selector with memristors to form a 2-selector-1-memristor structure and demonstrated several learning rules. These outstanding characteristics indicate that our selector has the potential to enable large scale neuromorphic networks.

Although grapevine (Vitis vinifera L.) is generally classified as a non-climacteric fruit, the regulatory mechanisms of ethylene in the ripening of non-climacteric fruit are still poorly understood. In this study, exogenous ethephon (ETH) strongly stimulated fruit color and anthocyanin accumulation, which was consistent with the increased expression of anthocyanin structural, regulatory, and transport genes. ETH application increased ABA content and decreased IAA content by coordinating ABA and auxin biosynthesis regulatory network. ETH treatment also accelerated sugar (glucose and fructose) accumulation by enhancing the gene expression involved in sugar transport and sucrose cleavage. ETH treatment blocked the synthesis of cellulose and accelerated the degradation of pectin, which was strongly associated with berry softening. To further confirm the function of ethylene biosynthesis and signaling genes, transient overexpression of VvACO4 and VvEIL3 were performed in both in tomato and strawberry fruits. These findings of the ethylene cascade add to our understanding of ethylene in non-climacteric berry ripening regulation and revealed a complex involvement of ethylene and its interplay with phytohormones during grapevine berry ripening.

Memristors show great promise in neuromorphic computing owing to their high-density integration, fast computing and low-energy consumption. However, the non-ideal update of synaptic weight in memristor devices, including nonlinearity, asymmetry and device variation, still poses challenges to thein-situlearning of memristors, thereby limiting their broad applications. Although the existing offline learning schemes can avoid this problem by transferring the weight optimization process into cloud, it is difficult to adapt to unseen tasks and uncertain environments. Here, we propose a bi-level meta-learning scheme that can alleviate the non-ideal update problem, and achieve fast adaptation and high accuracy, named Rapid One-step Adaption (ROA). By introducing a special regularization constraint and a dynamic learning rate strategy forin-situlearning, the ROA method effectively combines offline pre-training and online rapid one-step adaption. Furthermore, we implemented it on memristor-based neural networks to solve few-shot learning tasks, proving its superiority over the pure offline and online schemes under noisy conditions. This method can solvein-situlearning in non-ideal memristor networks, providing potential applications of on-chip neuromorphic learning and edge computing.

Transition metal elements doping can greatly improve the usability of Sb–Te for phase change memory. In this paper, we focused on the thermal stability of transition vanadium (V)-doped Sb2Te (VSb2Te) films with different V concentration which were prepared by co-sputtering. The effects of V doping on the crystallization and microstructure properties of Sb2Te were studied by temperature-dependent resistance measurements, in-situ heating TEM and HRTEM observation, respectively. With increasing V content, the crystallization is inhibited and the data retention is obviously enhanced, and the grain size shrinks significantly. Ab initio molecular dynamics simulation was used to explain the mechanism of the improved thermal stability and the results show that the diffusion of Sb and Te atoms are suppressed by doped V, which may account for the high thermal stability of VSb2Te.

Neuromorphic computing has emerged as a highly promising alternative to conventional computing. The key to constructing a large-scale neural network in hardware for neuromorphic computing is to develop artificial neurons with leaky integrate-and-fire behavior and artificial synapses with synaptic plasticity using nanodevices. So far, these two basic computing elements have been built in separate devices using different materials and technologies, which poses a significant challenge to system design and manufacturing. In this work, we designed a resistive device embedded with an innovative nano-vacuum gap between a bottom electrode and a mixed-ionic-electronic-conductor (MIEC) layer. Through redox reaction on the MIEC surface, metallic filaments dynamically grew within the nano-vacuum gap. The nano-vacuum gap provided an additional control factor for controlling the evolution dynamics of metallic filaments by tuning the electron tunneling efficiency, in analogy to a pseudo-three-terminal device, resulting in tunable switching behavior in various forms from volatile to nonvolatile switching in a single device. Our device demonstrated cross-functions, in particular, tunable neuronal firing and synaptic plasticity on demand, providing seamless integration for building large-scale artificial neural networks for neuromorphic computing.

Atomic switches are promising candidates as the basic building blocks for large-scale neuromorphic networks due to their tunable switching behaviors. Several neuromorphic components based on atomic switches have been demonstrated, including artificial synapses, artificial neurons and short-term to long-term memory, making it possible to construct neuromorphic systems using a unified device. Although the mechanism of atomic switches has been actively studied, most of the discussions in previous studies are qualitative and failed to provide a comprehensive view of the dynamics that can precisely describe the metallic bridging under an electric field. In this paper, we designed a gap-type atomic switch and realized various switching behaviors, including both volatile and non-volatile resistive switching. Employing advanced microanalysis technology, we experimentally studied the switching mechanism and captured the nanoscale metallic filament in gap-type atomic switches. Furthermore, based on the experimental findings as well as on the electrochemistry fundamental and electron tunneling effect, we proposed a physical model that precisely reproduced the sophisticated switching behaviors. Our model mathematically described the growth/shrinkage dynamics of nanoscale metallic filaments, providing a direction for studying the switching behaviors from a quantitative view. The simulation results are in good agreement with the experimental findings in both DC sweep and pulse operation modes. In addition, we have demonstrated neuronal tonic spiking and short-term to long-term memory in experiment and simulation, indicating that our model can be applied to the circuit level simulation of large-scale atomic switch arrays for neuromorphic applications.

Thin-film architectures are a staple in a wide range of technologies, such as semiconductor devices, optical coatings, magnetic recording, solar cells and batteries. Despite the industrial success of thin-film technology, mostly due to the easy fabrication and low cost, a fundamental drawback remains: it is challenging to alter the features of the film once fabricated. Here we report a methodology to modify the thickness and sequence of the innermost solid-state thin-film layers. We start with a thin-film stack of amorphous iron oxide and silver. By applying a suitable voltage bias and then reversing it, we can float the silver layer above or below the oxide layer by virtue of the migration of silver atoms. Scanning transmission electron microscopy reveals various sequences and thicknesses of the silver and oxide layers achieved with different experimental conditions. As a proof-of-principle, we show a dynamic change of structural colours of the stack derived from this process. Our results may offer opportunities to dynamically reconfigure thin-film-based functional nanodevices in situ.

The widespread implementation and rapid development of autonomous systems pose stringent performance requirements on emerging sensory systems. In addition to the basic sensing requirements, leading sensory systems are required to process data and extract featured information from highly redundant data in real time. With the added edge‐computational capabilities, data shuttling is avoided, leading to significant reduction of computational burden and bandwidth pressure in the cloud. Among the different computing architectures, the neuromorphic sensory system stands out due to its high power efficiency, low latency, and excellent processing capability. Mimicking the biological neural network, the colocation of sensory, processor, and memory components of neuromorphic sensory systems enables the requirements for frequent data shuttles to be circumvented. In particular, artificial intelligent perceptions built on memristive neuromorphic systems exhibit outstanding characteristics of small footprint, low power consumption, 3D stacking ability, and high density. Herein, the two essential parts of the memristive artificial perceptron system are presented: 1) memristive systems for neuromorphic computing and 2) high‐performance sensors. Next, the current state of the art established on artificial perceptron systems covering visual, auditory, and tactile sensations is highlighted. To conclude, the current challenges and future direction in the area of advanced intelligent perceptions are presented.

Physically transient electronics, which can disappear under certain conditions in aqueous solutions or biofluids, has attracted increasing attention because of its potential applications as "green" electronics and biomedical devices. Till now, the excitation of the transient process is achieved by passive dissolution of the encapsulation layer, which has a very limited control over the process. Here, we report a novel light-triggered encapsulation strategy via a bilayer of a light-responsive hydrogel and oxide to control the degradation on demand in aqueous environment. The hydrogel serving as a barrier between the environment and oxide limited the water's movement and penetration, leading to improved stable operation time. More importantly, the light-responsive hydrogel underwent a gel-to-solution transition upon applying ultraviolet (UV) light. The drastic change of the water movement enabled a transient process triggered on demand. Via this encapsulation scheme, we demonstrated fully soluble resistors and resistive random access memory devices with the UV light-triggered transient process. This work provides a new pathway to design transient devices with controllable degradation to meet various requirements of green electronics and biomedical devices.

Physically transient electronics have attracted increasing attention recently due to their potential as the basis for building “green” electronics and biomedical devices. In the development of tran...

The contradictory between switching speed and thermal stability has long been a huge challenge for phase-change memory applications. For providing a feasible solution, the rapid-phase-transition material — pure antimony, was incorporated with silicon carbide to complement their advantages. The characterization results elucidate that dopants situated in the grain boundary inhibit the crystal growth and enhance the stability of the temperature-sensitive amorphous state. Convincingly for devices, an ultrafast speed of 7 ns, an operational voltage requirement of only 1.0 V, a high endurance of more than 200 K and a long data retention are all demonstrated to be realizable and repeatable.

Non-spherical silica particles (NSSPs) have shown great advantages in chemical mechanical polishing (CMP) as abrasive particles. Due to their novel shapes, the material removal rate (MRR) is enhanced dramatically. In this paper, we propose a facile approach to synthesize NSSPs with verified effectiveness in polishing test and considerable potential in industrial production. In our experiments, zinc acetate was introduced into the traditional Stober system as morphology controller to form peanuts shaped NSSPs, whose sizes were investigated with the concentration of ammonium hydroxide, water and Zn(Ac)2. Results show that the novel shapes of NSSPs originate from the overlap of fractal spherical silica particles in the early stage of Stober reaction. Hereafter, by studying the structure of NSSPs by transmission electron microscope (TEM) and electron scattering, the fractal nature of NSSPs has been revealed, demonstrating a similar result compared with small angle X-ray scattering (SAXS). To our knowledge, this is the first study on the effect of divalent ions on the Stober system and is the first attempt to interpret the fractal nature of amorphous materials by electron scattering.

Neuromorphic computing has attracted increasing attention in medical applications due to its ability to improve diagnosis accuracy and human healthcare monitoring. However, the current remote operation mode has a time delay between in vivo data acquisition and in vitro clinical decision-making. Thus, it is of great importance to build a biodegradable neuromorphic network that can operate in a local physiological environment. A biodegradable synapse is a crucial component of such neuromorphic networks. However, the materials employed currently to develop a biodegradable synapse are incompatible with the foundry process, making it challenging to achieve a high density and large-scale neuromorphic network. Here, we report a biodegradable artificial synapse based on a W/Cu/WO3/SiO2/W structure, which is constructed from materials widely used in advanced semiconductor foundries. The device exhibits resistive switching, and the dominated mechanisms are attributed to Ohm's law and trap-filled space charge limited conduction. By manipulating pulse amplitudes, widths, and intervals, the device conductance can be finely regulated to achieve various synaptic functions, such as long-term potentiation, long term depression, paired-pulse facilitation, and spike-rate-dependent plasticity. Moreover, the learning-forgetting-relearning process, which is an essential and complex synaptic behavior, is emulated in a single device. Pattern learning of a slash symbol is also accomplished by building a 4 × 4 synaptic array. In addition, the systematic solubility testing proves its full biodegradability in biofluids. This work opens a potential pathway toward the integration of large-scale neuromorphic network for bioelectronics.

Thin film selector is an indispensable part for high density memory and memristor arrays to avoid the sneak path problem in crosspoint architecture. Although significant efforts have been devoted to developing novel thin-film selectors, it remains a great challenge in achieving good switching performance in the selectors to satisfy diverse memory elements. In this work, we utilized high-defect-density chalcogenide glass in conjunction with high mobility metal element to achieve a super nonlinear selective switching. The proposed thin-film selector based on the mixed ionic electronic conducting material (MIEC) exhibits superior selecting performance including excellent nonlinearity (< 5 mV/dev), ultra-low leakage (< 10 fA), and bidirectional operation. A switching model is proposed, and the in-depth understanding of the selective switching mechanism offers an insight of switching dynamics for MIEC thin-film selector. This work opens a new direction of selector design by combining high mobility elements and high-defect-density chalcogenide glasses, which can be extended to other materials with similar properties.

Neuromorphic systems aim to implement large-scale artificial neural network on hardware to ultimately realize human-level intelligence. The recent development of nonsilicon nanodevices has opened the huge potential of full memristive neural networks (FMNN), consisting of memristive neurons and synapses, for neuromorphic applications. Unlike the widely reported memristive synapses, the development of artificial neurons on memristive devices has less progress. Sophisticated neural dynamics is the major obstacle behind the lagging. Here a rich dynamics-driven artificial neuron is demonstrated, which successfully emulates partial essential neural features of neural processing, including leaky integration, automatic threshold-driven fire, and self-recovery, in a unified manner. The realization of bioplausible artificial neurons on a single device with ultralow power consumption paves the way for constructing energy-efficient large-scale FMNN and may boost the development of neuromorphic systems with high density, low power, and fast speed.

Selector elements with high nonlinearity are an indispensable part in constructing high density, large-scale, 3D stackable emerging nonvolatile memory and neuromorphic network. Although significant efforts have been devoted to developing novel thin-film selectors, it remains a great challenge in achieving good switching performance in the selectors to satisfy the stringent electrical criteria of diverse memory elements. In this work, we utilized high-defect-density chalcogenide glass (Ge2Sb2Te5) in conjunction with high mobility Ag element (Ag-GST) to achieve a super nonlinear selective switching. A novel electrodeposition–diffusion dynamic selector based on Ag-GST exhibits superior selecting performance including excellent nonlinearity (

Phase change memory (PCM) is one of the most promising candidates for the next-generation non-volatile memory, of which phase change material as the storage media is the key factor. In this paper, we proposed a novel phase change material CrxSb2Te1, which exhibits faster phase transition speed, better thermal stability and higher reliability compared with traditional Ge2Sb2Te5. With the increase of doped-Cr, the grain sizes are refined and the volume change rate decreases. What’s more, in the composition range studied, the crystal structure of CrxSb2Te1 films is single hexagonal phase without phase separation. For preferred Cr0.25Sb2Te1 and Cr0.41Sb2Te1 films, the resistance ratios between amorphous and crystalline states are up to two orders of magnitude or more; the temperatures for 10-year-data-rentation are 102.7 and 128.4 °C, respectively, showing good thermal stability performance. For the PCM cells based on the preferred Cr0.25Sb2Te1 and Cr0.41Sb2Te1 films, the threshold current/threshold voltage are about 4 μA/1.3 V and 4 μA/1.2 V, respectively; and when the voltage pulse width is short as 30 ns, the SET/RESET voltages are 1.7/3.3 and 1.9/4.5 V, respectively, showing high speed application potential for PCM.

Phase change materials rule the basic scientific issues in the research of phase change memory. As an important member in the phase change material system, GeTe is competitive for both technological applications and fundamental studies. However, its relatively poor thermal stability in the amorphous state and serious grain clustering need to be overcome for the application of GeTe. Here we propose Ge–Te–Ti (GTT) as a novel phase change material. For GTT, just with a small Ti fraction, the temperature for 10 year's data retention reaches 175 °C, and its grain size decreases one order of magnitude. Results of Raman scatting measurements indicate that the basic structure unit distribution of GTT deviates from the normal distribution to the Ge-rich direction with the Ti fraction increasing. The Ti-induced amorphous structure adjustment in GTT is the physical origin for the thermal stability enhancement, which makes GTT more extensively applicable in high temperature fields through the appropriate disordering adjustment.

Tungsten-doped Ge2Sb2Te5 (GSTW) semiconductor films with W concentrations of 3.2, 7.1, and 10.8%, deposited by cosputtering, have been proposed to improve the crystallization behavior of Ge2Sb2Te5 (GST) phase change materials. The crystalline resistances and crystallization temperatures of GST and GSTW films have been studied using in situ temperature-dependent resistance measurements. The intrinsic crystallization mechanism from amorphous to face-centered-cubic (FCC) structure has been discovered using temperature-dependent Raman spectra from 300 K to 720 K. It was found that GSTW films exhibit a more stable cubic geometry, better thermal stability of the amorphous state and higher 10 year data retention ability than pure GST. This could be due to the substitution of Te atoms or vacancies by W atoms in the crystal lattice, which leads to disorder of the crystalline structure and inhibits further crystallization.