Stabilizing the Ferroelectric Phase of Hf_{0.5}Zr_{0.5}O_{2} Thin Films by Charge Transfer.

Ferroelectric hafnia-based thin films have attracted significant interest due to their compatibility with complementary metal-oxide-semiconductor technology (CMOS). Achieving and stabilizing the metastable ferroelectric phase in these films is crucial for their application in ferroelectric devices. Recent research efforts have concentrated on the stabilization of the ferroelectric phase in hafnia-based films and delving into the mechanisms responsible for this stability. In this study, we experimentally demonstrate that stabilization of the ferroelectric phase in Hf_{0.5}Zr_{0.5}O_{2} (HZO) can be controlled by the interfacial charge transfer and the associated hole doping of HZO. Using the meticulously engineered charge transfer between an La_{1-x}Sr_{x}MnO_{3} buffer layer with variable Sr concentration x and an HZO film, we find the optimal x=0.33 that provides the required hole doping of HZO to most efficiently stabilize its ferroelectric phase. Our theoretical modeling reveals that the competition of the hole distribution between the threefold and fourfold coordinated oxygen sites in HZO controls the enhancement or reduction of the ferroelectric phase. Our findings offer a novel strategy to stabilize the ferroelectric phase of hafnia-based films and provide new insights into the development of ferroelectric devices compatible with CMOS.

A bidirectional thermal sensory leaky integrate-and-fire (LIF) neuron model based on bipolar NbOx volatile threshold devices with ultra-low operating current.

Brain-like artificial intelligence (AI) will become the main form and important platform in future computing. It will play an important and unique role in simulating brain functions, efficiently implementing AI algorithms, and improving computing power. Developing artificial neurons that can send facilitation/depression signals to artificial synapses, sense, and process temperature information is of great significance for achieving more efficient and compact brain-like computing systems. Herein, we have constructed a NbOx bipolar volatile threshold memristor, which could be operated by 1 µA ultra-low current and up to ∼104 switching ratios. By using a leaky integrate-and-fire (LIF) artificial neuron model, a bipolar LIF artificial neuron is constructed, which can realize the conventional threshold-driven firing, all-or-nothing spiking, refractory periods, and intensity-modulated frequency response bidirectionally at the positive/negative voltage stimulation, which will give the artificial synapse facilitation/depression signals. Furthermore, this bipolar LIF neuron can also explore different temperatures to output different signals, which could be constructed as a more compact thermal sensory neuron to avoid external harm to artificial robots. This study is of great significance for improving the computational efficiency of the system more effectively, achieving high integration density and low energy consumption artificial neural networks to meet the needs of brain-like neural computing.

Silicon based Bi0.9La0.1FeO3 ferroelectric tunnel junction memristor for convolutional neural network application.

Computing in memory (CIM) based on memristors is expected to completely solve the dilemma caused by von Neumann architecture. However, the performance of memristors based on traditional conductive filament mechanism is unstable. In this study, we report a nonvolatile high-performance memristor based on ferroelectric tunnel junction (FTJ) Pd/Bi0.9La0.1FeO3 (6.9 nm) (BLFO)/La0.67Sr0.33MnO3 (LSMO) on a silicon substrate. The conductance of this device was adjusted by different pulse stimulation parameter to achieve various synaptic functions because of ferroelectric polarization reversal. Based on the multiple conductance characteristics of the devices and the high linearity and symmetry of weight updating, image processing and VGG8 convolutional neural network (CNN) simulation based on the devices were realized. Excellent results of the image processing are demonstrated. The recognition accuracy of CNN offline learning reached an astonishing 92.07% based on Cifar-10 dataset. This provides a more feasible solution to break through the bottleneck of von Neumann architecture.

Interface-engineered ferroelectricity of epitaxial Hf0.5Zr0.5O2 thin films.

Ferroelectric hafnia-based thin films have attracted intense attention due to their compatibility with complementary metal-oxide-semiconductor technology. However, the ferroelectric orthorhombic phase is thermodynamically metastable. Various efforts have been made to stabilize the ferroelectric orthorhombic phase of hafnia-based films such as controlling the growth kinetics and mechanical confinement. Here, we demonstrate a key interface engineering strategy to stabilize and enhance the ferroelectric orthorhombic phase of the Hf0.5Zr0.5O2 thin film by deliberately controlling the termination of the bottom La0.67Sr0.33MnO3 layer. We find that the Hf0.5Zr0.5O2 films on the MnO2-terminated La0.67Sr0.33MnO3 have more ferroelectric orthorhombic phase than those on the LaSrO-terminated La0.67Sr0.33MnO3, while with no wake-up effect. Even though the Hf0.5Zr0.5O2 thickness is as thin as 1.5 nm, the clear ferroelectric orthorhombic (111) orientation is observed on the MnO2 termination. Our transmission electron microscopy characterization and theoretical modelling reveal that reconstruction at the Hf0.5Zr0.5O2/ La0.67Sr0.33MnO3 interface and hole doping of the Hf0.5Zr0.5O2 layer resulting from the MnO2 interface termination are responsible for the stabilization of the metastable ferroelectric phase of Hf0.5Zr0.5O2. We anticipate that these results will inspire further studies of interface-engineered hafnia-based systems.