Ceramic matrix composites have the advantages of low density, high specific strength, high specific die, high-temperature resistance, wear resistance, chemical corrosion resistance, etc., which are widely used in aerospace, energy, transportation, and other fields. CMCs have become an important choice for engine components and other high-temperature component manufacturing. However, ceramic matrix composite is a kind of multi-phase structure, anisotropy, high hardness material, due to the brittleness of the ceramic matrix, the weak bonding force between fiber and matrix, and the anisotropy of composite material. Burr, delamination, tearing, chips, and other surface damage tend to generate in the machining, resulting in surface quality and strength decline. This paper reviewed the latest abrasive machining technology for SiC ceramic composites. The characteristics and research directions of the main abrasive machining technology, including grinding, laser-assisted grinding, ultrasonic-assisted grinding, and abrasive waterjet machining, are introduced first. Then, the commonly used numerical simulation research for modeling and simulating the machining of ceramic matrix composites is briefly summarized. Finally, the processing difficulties and research hotspots of ceramic matrix composites are summarized.

Lead sulfide quantum dots (PbS QDs), as a functional-layer coating, enable non-volatile integration and neuromorphic computing in memristive structures to address the von Neumann bottleneck. Herein, the dual-interface mechanism of PbS QDs in the memristor film structure is reviewed. First, the local electric field enhancement effect generates tip electrode-like structures in the coating film through QD-mediated spatial charge gradients, thereby enabling precise control over the nucleation and growth of conductive filaments (CFs). As a result, the consistency of switching voltages and the thermal stability at elevated temperatures are significantly improved. Conversely, the anion reservoir effect exploits surface dangling bonds on QDs to efficiently capture anions from the dielectric layer, thereby synergistically regulating vacancy migration kinetics. This process enables zero-initialization behavior and ultra-low-power operation. In addition, the spatial distribution design and density modulation of QDs further reinforce both mechanisms. The structural optimization of QD/dielectric interface engineering can simultaneously improve cycling endurance and resistive switching uniformity. Furthermore, modification of QD surface chemistry through ligand decoration and passivation suppresses the stochasticity of ionic diffusion while improving the linearity of synaptic weight updates. This interfacial engineering strategy utilizing QDs as coating films advances the development of high-performance photonic–electronic systems for memory–computing convergence.

Optoelectronic memristors show significant potential for neuromorphic computing but are often limited by high operating voltages. Here, we constructed a low-voltage Ag/CsPbBr₃/CDs/FTO photonic memristor by incorporating carbon dots (CDs) as a regulatory medium within the CsPbBr₃ perovskite layer. The CDs significantly enhanced memristive performance: their surface defect states and localized electric field regulation enabled stable conductive filament formation and rupture at low voltages. This yielded favorable operational characteristics, including low set/reset voltages (0.72 V/−0.60 V) and preliminary cycling endurance with state retention. Leveraging the device’s continuously tunable conductance, we successfully emulated key synaptic learning behaviors such as paired-pulse facilitation (PPF) and long-term potentiation/depression (LTP/LTD). Implementing its weight-updating characteristics in an artificial neural network (ANN) achieved 92.17% accuracy in handwritten digit classification. This work demonstrates the effective role of CDs in enabling low-voltage, high-performance perovskite photonic memristors, providing a viable strategy for developing low-power neuromorphic hardware.