The development of the resistive switching cross-point array as the next-generation platform for high-density storage, in-memory computing and neuromorphic computing heavily relies on the improvement of the two component devices, volatile selector and nonvolatile memory, which have distinct operating current requirements. The perennial current-volatility dilemma that has been widely faced in various device implementations remains a major bottleneck. Here, we show that the device based on electrochemically active, low-thermal conductivity and low-melting temperature semiconducting tellurium filament can solve this dilemma, being able to function as either selector or memory in respective desired current ranges. Furthermore, we demonstrate one-selector-one-resistor behavior in a tandem of two identical Te-based devices, indicating the potential of Te-based device as a universal array building block. These nonconventional phenomena can be understood from a combination of unique electrical-thermal properties in Te. Preliminary device optimization efforts also indicate large and unique design space for Te-based resistive switching devices.

Magnetometers combined with inertial sensors are widely used for orientation estimation, and calibrations are necessary to achieve high accuracy. This paper presents a complete tri-axis magnetometer calibration algorithm with a gyro auxiliary. The magnetic distortions and sensor errors, including the misalignment error between the magnetometer and assembled platform, are compensated after calibration. With the gyro auxiliary, the magnetometer linear interpolation outputs are calculated, and the error parameters are evaluated under linear operations of magnetometer interpolation outputs. The simulation and experiment are performed to illustrate the efficiency of the algorithm. After calibration, the heading errors calculated by magnetometers are reduced to 0.5° (1σ). This calibration algorithm can also be applied to tri-axis accelerometers whose error model is similar to tri-axis magnetometers.

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Microsystems play an important role in the Internet of Things (IoT). In many unattended IoT applications, microsystems with small size, lightweight, and long life are urgently needed to achieve covert, large-scale, and long-term distribution for target detection and recognition. This paper presents for the first time a low-power, long-life microsystem that integrates self-power supply, event wake-up, continuous vibration sensing, and target recognition. The microsystem is mainly used for unattended long-term target perception and recognition. A composite energy source of solar energy and battery is designed to achieve self-powering. The microsystem’s sensing module, circuit module, signal processing module, and transceiver module are optimized to further realize the small size and low-power consumption. A low-computational recognition algorithm based on support vector machine learning is designed and ported into the microsystem. Taking the pedestrian, wheeled vehicle, and tracked vehicle as targets, the proposed microsystem of 15 cm3 and 35 g successfully realizes target recognitions both indoors and outdoors with an accuracy rate of over 84% and 65%, respectively. Self-powering of the microsystem is up to 22.7 mW under the midday sunlight, and 11 min self-powering can maintain 24 h operation of the microsystem in sleep mode.

Exciton-exciton annihilation (EEA), as typical nonradiative recombination, plays an unpopular role in semiconductors. The nonradiative process significantly reduces the quantum yield of photoluminescence, which substantially inhibits the maximum efficiency of...

Information in conventional digital computing platforms is encoded in the steady states of transistors and processed in a quasi-static way. Memristors are a class of emerging devices that naturally embody dynamics through their internal electrophyiscal processes, enabling nonconventional computing paradigms with enhanced capability and energy efficiency, such as reservoir computing. Here, we report on a dynamic memristor based on LiNbO3. The device has nonlinear I-V characteristics and exhibits short-term memory, suitable for application in reservoir computing. By time multiplexing, a single device can serve as a reservoir with rich dynamics which used to require a large number of interconnected nodes. The collective states of five memristors after the application of trains of pulses to the respective memristors are unique for each combination of pulse patterns, which is suitable for sequence data classification, as demonstrated in a 5 × 4 digit image recognition task. This work broadens the spectrum of memristive materials for neuromorphic computing.

The electrochemical (EC) resistive switching (RS) cross-point arrays, composed of nonvolatile RS (NV-RS) memories and volatile RS (V-RS) selectors, hold promise for high-density data storage, in-memory computing and neuromorphic computing. However, the conventional EC-RS devices based on metallic filaments suffer from the notorious current-volatility dilemma that the low and high current requirements for NV-RS memories and V-RS selectors, respectively, cannot be satisfied simultaneously, due to the dominant EC nature of the RS. In this work, we demonstrate electrochemically active, low thermal-conductivity and low melting-temperature semiconducting tellurium filament-based RS devices that solve this dilemma, enabling NV-RS memories to operate under lower currents than do V-RS selectors. This novel phenomenon arises as the consequence of the adversarial EC and Joule heating (JH) effects. The devices also show unusual stimulus frequency dependent long-term plasticity (LTP)-to-short-term plasticity (STP) transition. Devices with this property can be generically utilized as spatial-temporal filters in spiking neural networks (SNNs) for high-performance event-based visual recognition tasks, as illustrated in our noise filtering simulations. By regulating the EC-JH relationship using dielectric materials with decreasing thermal conductivities, full functional-range tunable Te filament-based devices, from always-NV RS, to NV-to-V transitionable RS, and to always-V RS, are also demonstrated. The tellurium filament-based RS devices are promising enablers for functional cross-point arrays.