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To enhance the fracture toughness of B4C ceramics, B4C/TiB2 ceramics with excellent performance were prepared by using TiO2 as the addition item and using spark plasma sintering to introduce TiB2 based on in situ reaction, which was compared with ceramics directly introduced with TiB2, the impacts of TiO2 content, temperature and holding time on B4C/TiB2 ceramics were studied, and the toughening mechanism of B4C/TiB2 ceramics in situ reaction was discussed. The results showed that compared with the ceramics directly introduced with TiB2, B4C/TiB2 ceramics with reinforcement phase TiB2 introduced through in‐situ reaction were easier to obtain higher relative density and better properties. Due to the existence of a discharge effect and the application of pulse current, B4C/TiB2 ceramics with excellent comprehensive properties can be prepared at lower temperatures and short time. When 15 vol% TiO2 was added, the temperature was 1750°C and the holding time was 10 min, the comprehensive properties were better, and their relative density, Vickers hardness, flexural strength, and fracture toughness were 98.5%, 27.72 GPa, 474 MPa, and 5.08 MPa·m1/2, respectively. Toughening mechanisms such as crack deflection, crack branching, and crack bridging improved the fracture toughness. [ABSTRACT FROM AUTHOR]

Tunable and uniform evolution of conductance is the key performance metric for neuromorphic computing leveraging memristors. Nonetheless, the stochastic conductance update associated with limited material composition and uncontrollable filament distribution has restricted the tunability that can be customized for targeted synaptic properties. Here, we introduce organic heterojunction memristors utilizing the C60/P3HT bilayer, demonstrating analog switching characteristics with multilevel conductance states. We demonstrate that both conventional bipolar and unipolar voltages can achieve synaptic plasticity modulation for potentiation and depression, offering enhanced tunability. Through in situ Raman spectroscopy and impedance spectroscopy, we directly observe the dynamic alterations within the active layers during switching processes. The reversible migration of ions diminishes the barrier within the polymer layer, leading to highly uniform resistive switching behavior. The C60 layer functions as a confined transport medium, mitigating critical current variability issues. Moreover, we introduce a shunt resistor approach, furnishing analog memristors with selectively adjustable uniformity, enhanced linearity, and expanded dynamic conductance range, providing a general solution adaptable to various memristive hardware architectures. [ABSTRACT FROM AUTHOR]

The rapid advancement of neuromorphic computing demands innovative hardware solutions capable of efficiently mimicking the functionality of biological neural systems. In this context, dynamic memristors have emerged as promising candidates for realizing neuromorphic reservoir computing (RC) architectures. The dynamic memristors characterized by their ability to exhibit nonlinear conductance variations and transient memory behaviors offer unique advantages for constructing RC systems. Unlike recurrent neural networks (RNNs) that face challenges such as vanishing or exploding gradients during training, RC leverages a fixed‐size reservoir layer that acts as a nonlinear dynamic memory. Researchers can capitalize on their adaptable and efficient characteristics by integrating dynamic memristors into RC systems to enable rapid information processing with low learning costs. This perspective provides an overview of the recent developments in dynamic memristors and their applications in neuromorphic RC. It highlights their potential to revolutionize artificial intelligence hardware by offering faster learning speeds and enhanced energy efficiency. Furthermore, it discusses challenges and opportunities associated with integrating dynamic memristors into RC architectures, paving the way for developing next‐generation cognitive computing systems. [ABSTRACT FROM AUTHOR]

Organic memristors with uniform resistive switching behavior can mimic the functions of biological synapses, making them promising candidates for brain-inspired neuromorphic computing. Among these, organic memristors based on ion migration mechanisms have been widely studied because of the directional movement of anions such as metal cations and oxygen ions under electric fields, which is more similar to the biological process of ion release in synapses. However, controlling these ion dynamic processes is often challenging, with rapid ion migration leading to unstable device performance and reduced accuracy. Here, we present the fabrication of a memristor utilizing an organic heterojunction. The built-in electric field generated by the heterojunction slows down and orders the ion migration process, enabling the device to exhibit a wide range of precise multiple conductive states (10−6–10−3 A). This provides an advantage in simulating synaptic behavior. The continuously adjustable conductance in organic heterojunction memristors successfully mimics various forms of synaptic plasticity, including paired-pulse facilitation (PPF), post-tetanic potentiation (PTP), short-term plasticity (STP) and long-term plasticity (LTP), as well as higher-order functionalities such as simulating Pavlovian classical conditioning reflex experiment and experiential learning function. Furthermore, by utilizing X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary-ion mass spectrometry (ToF-SIMS) depth profiling, we have gained clear insights into the distribution and movement of ions of different compositions within the device, elucidating the underlying reasons for resistance changes. These findings provide reliable references and demonstrations for the design and control of future ion migration-type memristors. [ABSTRACT FROM AUTHOR]