Polymer memristors with light weight and mechanical flexibility are preeminent candidates for low-power edge computing paradigms. However, the structural inhomogeneity of most polymers usually leads to random resistive switching characteristics, which lowers the production yield and reliability of nanoscale devices. In this contribution, we report that by adopting the two-dimensional conjugation strategy, a record high 90% production yield of polymer memristors has been achieved with miniaturization and low power potentials. By constructing coplanar macromolecules with 2D conjugated thiophene derivatives to enhance the π–π stacking and crystallinity of the thin film, homogeneous switching takes place across the entire polymer layer, with fast responses in 32 ns, D2D variation down to 3.16% ~ 8.29%, production yield approaching 90%, and scalability into 100 nm scale with tiny power consumption of ~ 10−15 J/bit. The polymer memristor array is capable of acting as both the arithmetic-logic element and multiply-accumulate accelerator for neuromorphic computing tasks.
Owing to the advantages of high efficiency, high energy density, electrical isolation, low electromagnetic interference (EMI) and harmonic pollution, magnetic integration, wide output ranges, low voltage stress, and high operation frequency, the LLC resonant converters are widely used in various sectors of the electronics-based industries. The history and development of the LLC resonant converters are presented, their advantages are analyzed, three of the most popular LLC resonant converter topologies with detailed assessments of their strengths and drawbacks are elaborated. Furthermore, an important piece of research on the industrial applications of the LLC resonant converters is conducted, mainly including electric vehicle (EV) charging, photovoltaic systems, and light emitting diode (LED) lighting drivers and liquid crystal display (LCD) TV power supplies. Finally, the future evolution of the LLC resonant converter technology is discussed.
In-sensor computing can simultaneously output image information and recognition results through in-situ visual signal processing, which can greatly improve the efficiency of machine vision. However, in-sensor computing is challenging due to the requirement to controllably adjust the sensor’s photosensitivity. Herein, it is demonstrated a ternary cationic halide Cs0.05FA0.81MA0.14 Pb(I0.85Br0.15)3 (CsFAMA) perovskite, whose External quantum efficiency (EQE) value is above 80% in the entire visible region (400–750 nm), and peak responsibility value at 750 nm reaches 0.45 A/W. In addition, the device can achieve a 50-fold enhancement of the photoresponsibility under the same illumination by adjusting the internal ion migration and readout voltage. A proof-of-concept visually enhanced neural network system is demonstrated through the switchable photosensitivity of the perovskite sensor array, which can simultaneously optimize imaging and recognition results and improve object recognition accuracy by 17% in low-light environments.
This paper presents and analyzes an AC-DC power converter structure, which is comprised of a Power Factor Correction (PFC) module and a LLC resonant DC-DC converter module. This converter only uses two switches, and requires three less diodes and one less switch compared to popular LLC resonant converter solutions. Compared to its conventional counterpart, the rectifier of interest has high energy efficiency while a smaller size, owing to the soft-switching in the LLC resonant converter. Detailed theoretical analyses are conducted in this study, followed by software simulation and hardware experimentation, which demonstrate that the single stage double-switched (DS)-LLC rectifier is able to realize unity power factor and a wide output range, indicating its effectiveness and applicability.
A high frequency and high efficiency DC-DC converter with sensorless adaptive-sizing technique is proposed. Instead of conventional adaptive-sizing technique with current sensor, the proposed converter estimates the load current according to the output voltage of error amplifier for switch scaling. The elimination of current sensor reduces power consumption, thus improving efficiency further. This design is validated through simulation in a 0.18 µm CMOS process. At switching frequency of 150 MHz and light-load of 20 mA, the proposed converter achieves a high efficiency of 82.4%, while it is 73.7% with conventional adaptivesizing technique, and 62.4% without adaptive-sizing technique.
This paper describes a four quadrant, 250kW(550V, 450A) switch-mode, zero current soft switching (ZCS) power converter for a 1.4 GeV beam transfer line magnet. The ZCS technique is the preferred approach for the high power switch-mode converters employing Insolated Gate Bipolar Transistors (IGBT) to reduce switching losses and EMI. This switch-mode power converter topology has been selected because of the high dynamic response, low output ripple, and low input current harmonics. In this paper, the circuit topology, function of the system components, key system specifications and experimental results for a 250kW switch-mode converter are described in detail. The experimental results include output current transient response and conducted electromagnetic interference (EMI, measurements at both AC input and DC output). The design and development process employed is based on virtual electrical simulation of the system. This technique has been essential for the successful development of this unit.
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