The effect of the γ-ray total dose radiation on the energy storage density (ESD) and the phase transition of antiferroelectric-like (AFE-like) Al-doped HfO₂ (HfAlO) thin films was investigated.

Compensated co-doping not only achieves charge neutrality but is also highly favorable for the formation of polar phases and the absence of structural defects.

Conventional von Neumann architecture-based computing hardware faces high power consumption challenges and the mismatch between memory and computation performance when handling increasingly large and complex datasets. The rise of artificial neural networks (ANN) has driven the development of bio-inspired memory devices, such as artificial synapses, offering new opportunities to overcome the limitations of the post-Moore era. Ferroelectric memcapacitors, which store data based on capacitive principles, provide advantages such as resistance to read disturbance and low static power consumption compared to memristors, making them highly attractive for ANN applications. In this work, we fabricated Zr-doped HfO₂ (HZO) ferroelectric memcapacitive synaptic devices on mica substrate featuring a simple metal-ferroelectric-metal structure. We investigated the impact of bending on the ferroelectric performance and, through finite element analysis, found that stress concentration might lead to an increase in grain boundary defects, resulting in excess oxygen vacancies and a slight decrease in ferroelectric polarization. Additionally, we studied the memcapacitive characteristics of the HZO ferroelectric synaptic devices and successfully obtained the potentiation and depression characteristics. The synaptic plasticity curves were also fitted by least squares to obtain asymmetries of 0.030 and 0.068 for the flat and tensile states, respectively. The devices demonstrated remarkable neuromorphic computing capabilities, reaching a digital recognition accuracy as high as 97.63%. Notably, the maximum energy consumption per device was approximately 1.96 nJ in the training process and 4.03 pJ in the inference process. This study provides new directions for further developing flexible hafnium-based ferroelectric materials and devices. Graphical Abstract

Self-rectifying memristor (SRM) arrays hold tremendous potential in high-density data storage and energy-efficient neuromorphic computing. However, SRM arrays are mostly developed on rigid substrates and lack mechanical flexibility, limiting their applications in intelligent electronic skin and wearable technologies etc. Here, we present a high-performance SRM array based on Pt/HfO₂/Ta₂O_5-x/Ti heterojunctions, which can be fabricated on a flexible polyimides (PI) substrate and demonstrates exceptional memristive performance under bending conditions (R = 1 cm, rectifying ratio >10⁴, retention time >10⁴ s and endurance >10⁵ cycles). We demonstrate a 16×16 flexible memristor array offering noise filtering and data storage capabilities, which can be used to accurately process and store the signals transmitted by a pressure sensor array. This research represents an important advancement towards the realization of next-generation high-performance flexible electronics.

Oxygen vacancies play a crucial role in stabilizing the ferroelectric phase in hafnium (Hf) oxide-based thin films and in shaping the evolution of their ferroelectric properties. In this study, we directly manipulated the oxygen vacancy concentration in Hf_0.5Zr_0.5O_2−x (HZO) ferroelectric thin films in situ using oxygen plasma treatment. We scrutinized the variations in the ferroelectric properties of HZO films across different oxygen vacancy concentrations by integrating the findings from ferroelectric performance tests. Additionally, we elucidated the mechanism underlying the influence of oxygen vacancies on the coercive field and polarization properties of HZO ferroelectric films through the first-principles density functional theory (DFT) calculations. Finally, to study the impact of oxygen vacancies on the practical application of HZO ferroelectric synaptic devices, leveraging the plasticity of the ferroelectric polarization, we constructed a multilayer perceptron (MLP) network. We simulated its recognition accuracy and convergence speed under different oxygen vacancy concentrations in the MNIST recognition task.