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 (>10 9 ), record-high turn-on slope (<; 0.31 mV/dec.), excellent endurance (> 10 8 ) 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.
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.
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, the migration and redistribution of anions induces built-in electric field at 2D materials/electrode interface. It inevitably leads to nonlinearity and saturation of conductance change, which are the key challenges of 2D materials based synaptic devices to achieve high accuracy neuromorphic applications. In this work, we report a vertical heterostructure formed by monolayer CVD-grown MoS2 and WO3 films, in which the WO3 films serve as anions reservoir to steadily absorb and release sulfur anions, thus successfully overcoming the hurdles of nonlinearity and limited conductance states. We experimentally demonstrate a nearly linear change in conductance (∼1.1) and as high as 130 (∼27) weight states, which is a record among 2D materials-based synapses. Simulations prove that artificial neural network with MoS2/WO3 heterostructure synapses achieves a significantly improved learning accuracy of 93.2% in MNIST handwritten digits, demonstrating the dual benefits of linearity and multilevels caused by the anion reservoir. In addition, the essential synaptic behaviors, such as potentiation/depression, paired pulse facilitation, spike-rate-dependent plasticity as well as transformation from short-term plasticity to long-term plasticity are implemented in the heterostructure device. The introduction of anion reservoir opens an effective approach to overcome the limitations of 2D materials and enhance the performance of neuromorphic devices for high-precision neuromorphic computing.
As a growth-dominated phase change material, Sb4Te (ST) has fast crystallization speed while thermal stability is very poor, which makes it unsuitable for application in phase change random access memory (PCRAM). After doping Ti, the crystallization temperature is greatly improved to 210.33 °C, which is much higher than that of conventional Ge2Sb2Te5 (∼150 °C), and the melting point is reduced to 540.27 °C. In addition, grain size of crystalline Ti-doped Sb4Te (TST) film is significantly decreased to nanoscale. Ti atom is believed to occupy the lattice site of Sb atom in TST. With good thermal stability, TST-based PCRAM cell also has fast crystallization rate of 6 ns. Furthermore, the energy consumption is also lower than that of Ge2Sb2Te5-based one. Endurance of exceeding 2E5 cycles is obtained with a resistance ratio of one order of magnitude. Therefore, Ti doping seems to be a good way to solve the contradiction between thermal stability and fast crystallization speed of Sb-Te alloys.
Distribution hybrid lines, especially the underground cable sections, require higher fault location accuracy. Researches have shown that the length error of overhead lines has little effect on the online measurement of cable wave velocity, while the impact on the fault section identification process cannot be ignored, and the error factor of the overhead line length need to be considered to implement the correction of the time interval; utilize the fault point-section terminal (FST) fault location mode to replace the conventional fault point-line terminal (FLT) mode can effectively eliminate the fault location error and the offset of the line inspection point caused by the length error of the overhead line. Based on the above analysis, this paper proposes a corrected time interval and FST mode based travelling wave fault location algorithm for hybrid lines. The line inspection points could be accurately located by utilizing the actual measured time difference of the initial fault travelling wave reaching the measuring end and the corresponding fault section of each corrected time interval, and combining the online actual measured cable wave velocity under the condition of considering the length error of the overhead line. The simulation results indicate that the method can effectively implement the fault location of the hybrid line of the distribution network, and the result of which is accurate and reliable.
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.
To date, slow Set operation speed and high Reset operation power remain to be important limitations for substituting dynamic random access memory by phase change memory. Here, we demonstrate phase change memory cell based on Ti0.4Sb2Te3 alloy, showing one order of magnitude faster Set operation speed and as low as one-fifth Reset operation power, compared with Ge2Sb2Te5-based phase change memory cell at the same size. The enhancements may be rooted in the common presence of titanium-centred octahedral motifs in both amorphous and crystalline Ti0.4Sb2Te3 phases. The essentially unchanged local structures around the titanium atoms may be responsible for the significantly improved performance, as these structures could act as nucleation centres to facilitate a swift, low-energy order-disorder transition for the rest of the Sb-centred octahedrons. Our study may provide an alternative to the development of high-speed, low-power dynamic random access memory-like phase change memory technology. Ge2Sb2Te5 is widely studied and utilized in phase change memory. Here, the authors report one order of magnitude faster switching speed and as low as one-fifth reset operation power in a Ti-Sb-Te alloy, as compared to Ge2Sb2Te5.
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