In this work, a multi-stage material design framework combining machine learning techniques with density functional theory is established to reveal the mechanism of phase stabilization in HfO₂ based ferroelectric materials. The ferroelectric phase fractions based on a more stringent relationship of phase energy differences is proposed as an evaluation criterion for the ferroelectric performance of hafnium-based materials. Based on the Boltzmann distribution theory, the abstract phase energy difference is converted into an intuitive phase fraction distribution mapping. A large-scale prediction of unknown dopants is conducted within the material design framework, and gallium (Ga) is identified as a new dopant for HfO₂. Both experiments and density functional theory calculations demonstrate that Ga is an excellent dopant for ferroelectric hafnium oxide, especially, the experimentally determined variation trends of ferroelectric phase fraction and polarization properties with Ga doping concentration are in good agreement with the predictions given by machine learning. This work provides a new perspective from machine learning to deepen the understanding of the ferroelectric properties of HfO₂ materials, offering fresh insights into the design and performance prediction of HfO₂ ferroelectric thin films.

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

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.

In this paper, we designed double V-shaped Al/Ni multilayer energy-containing microdevices with different V-angles, and we performed finite element modeling and simulation of the heat transfer process of the designed energy-containing microdevice. Temperature-dependent resistivity was introduced to effectively simulate the phase change during ignition. We simulated the temperature and current density distribution in the central region of the Al/Ni multilayer energy-containing microdevice and predicted the ignition voltage threshold for the specific device structure. Al/Ni multilayer energy-containing microdevices with different V-angles were prepared by electron beam evaporation technology, and ignition experiments on the prepared devices under the excitation of 47 μF capacitance were conducted. The experimental results show that the critical voltage of ignition increases with the increase in the V-angle, which verifies the correctness of the proposed finite element model.