A novel power supply technology for 3D-integrated chips has been developed by employing a three-dimensionally stacked computing architecture consisting of processing units placed directly above stacks of dynamic random access memory. Towards realizing this, researchers developed key technologies involving precise and high-speed bonding technique and adhesive technology. These new technologies can help in addressing the demands of high-performance computing applications, which require both high memory bandwidth and low power consumption with reduced power supply noise.  

Photothermoelectric (PTE) photodetectors with self-powered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields. However, traditional PTE photodetectors lack of mechanical flexibility and cannot operate independently without the test instrument. Herein, we present a flexible PTE photodetector capable of dual-mode output, combining electrical and optical signal generation for enhanced functionality. Using solution processing, high-quality MXene thin films are assembled on asymmetric electrodes as the photosensitive layer. The geometrically asymmetric electrode design significantly enhances the responsivity, achieving 0.33 mA W^-1 under infrared illumination, twice that of the symmetrical configuration. This improvement stems from optimized photothermal conversion and an expanded temperature gradient. The PTE device maintains stable performance after 300 bending cycles, demonstrating excellent flexibility. A new energy conversion pathway has been established by coupling the photothermal conversion of MXene with thermochromic composite materials, leading to a real-time visualization of invisible infrared radiation. Leveraging this functionality, we demonstrate the first human–machine collaborative infrared imaging system, wherein the dual-mode photodetector arrays synchronously generate human-readable pattern and machine-readable pattern. Our study not only provides a new solution for functional integration of flexible photodetectors, but also sets a new benchmark for human–machine collaborative optoelectronics.

Researchers have developed an Event-triggered asymptotic composite neural tracking control scheme for intelligent vehicles, integrating radial basis function neural networks and a serial-parallel estimation model to compensate for system nonlinearities and uncertainties. Published in Advanced Equipment, this breakthrough has the potential to improve tracking precision of intelligent vehicles by ensuring asymptotic convergence of position error, enhance communication efficiency through event-triggered control, and boost system robustness against nonlinear dynamics and uncertainties, as validated by numerical simulations showing superior performance over traditional adaptive control methods.