Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles due to the intrinsic interwoven architecture and promising applications in wearable electronics. Developing reconfigurable fiber-based memristors is an efficient method to realize electronic textiles that capable of neuromorphic computing function. However, the previously reported artificial synapse and neuron need different materials and configurations, making it difficult to realize multiple functions in a single device. Herein, a textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics was reported, which can achieve both nonvolatile synaptic plasticity and volatile neuron functions. In addition, a single reconfigurable memristor can realize integrate-and-fire function, exhibiting significant advantages in reducing the complexity of neuron circuits. The firing energy consumption of fiber-based memristive neuron is 1.9 fJ/spike (femtojoule-level), which is at least three orders of magnitude lower than that of the reported biological and artificial neuron (picojoule-level). The ultralow energy consumption makes it possible to create an electronic neural network that reduces the energy consumption compared to human brain. By integrating the reconfigurable synapse, neuron and heating resistor, a smart textile system is successfully constructed for warm fabric application, providing a unique functional reconfiguration pathway toward the next-generation in-memory computing textile system.

Traditional von Neumann architecture is facing severe challenges due to separated physical structure of memory and processing units, which inspires the development of in‐memory computing electronics. Intriguingly, as a kind of complementary metal‐oxide‐semiconducor compatible ferroelectric material, HfO2 is widely studied based on first‐principles calculation in the semiconductor field, showing great potential in constructing emerging electronics. Different structures including ferroelectric diode, ferroelectric field effect transistor, and ferroelectric tunnel junctions based on HfO2 are proposed for in‐memory computing application. Here, this work reviews the progress of HfO2 from materials to devices, including crystal structure, fatigue mechanism, first‐principles calculation, and neuromorphic computing application of HfO2‐based device. This work can provide a reference for the HfO2 ferroelectric device development for next‐generation in‐memory computing applications.