arXiv subjects

Jianguo Yang

Publications and source records attributed to Jianguo Yang.

3 recordsLinked to original sources

arXiv2025

Nucleation suppression by charge screening on grain boundaries: a kinetic model for bulk imprint in polycrystalline ferroelectric thin films

The imprint effect, a significant reliability challenge in ferroelectric memories, manifests as a shift in the coercive field during retention and endurance tests, ultimately degrading the usable memory window. \rv{While traditional models attribute imprint primarily to charge screening at the interface between the dead layer and the ferroelectric film, the contribution from grain boundaries has been largely overlooked. This work advances a bulk imprint mechanism by establishing a phase-field model, which demonstrates that the tuning of domain nuclei near grain boundaries via charge screening consistently explains the imprint process and aligns with key experimental trends.} These findings provide novel insights into the imprint process and advance the understanding of reliability issues in ferroelectric memory devices.

cond-mat.mtrl-sci↗

arXiv2024

Intrinsic threshold electric field for domain wall motion in ferroelectrics based on discretized phase-field model

With the development of ferroelectric memories, it is becoming increasingly important to understand the ferroelectric switching behaviors at small applied electric fields. In this \rv{paper}, we use discretized phase-field models to systematically investigate the intrinsic threshold electric field (TEF) to drive flat 180$^\circ$ and 90$^\circ$ domain walls (DWs), which can not be captured by continuum models. The results show that this TEF increases as the ratio of DW width to unit cell size decreases, and it becomes significant if the DW width is thinner than two unit cells. The results are qualitatively consistent with existing first-principles studies and cryogenic experiments. In addition, this work proposes a conceptual model to explain the activation electric field (AEF) observed in experiments at room temperature. This work improves the understanding of DW motion kinetics at small applied fields, and shows that the mesh size and orientation are both important for the phase-field modeling of the above process.

cond-mat.mtrl-sci↗

arXiv2022

Efficient Training of the Memristive Deep Belief Net Immune to Non-Idealities of the Synaptic Devices

The tunability of conductance states of various emerging non-volatile memristive devices emulates the plasticity of biological synapses, making it promising in the hardware realization of large-scale neuromorphic systems. The inference of the neural network can be greatly accelerated by the vector-matrix multiplication (VMM) performed within a crossbar array of memristive devices in one step. Nevertheless, the implementation of the VMM needs complex peripheral circuits and the complexity further increases since non-idealities of memristive devices prevent precise conductance tuning (especially for the online training) and largely degrade the performance of the deep neural networks (DNNs). Here, we present an efficient online training method of the memristive deep belief net (DBN). The proposed memristive DBN uses stochastically binarized activations, reducing the complexity of peripheral circuits, and uses the contrastive divergence (CD) based gradient descent learning algorithm. The analog VMM and digital CD are performed separately in a mixed-signal hardware arrangement, making the memristive DBN high immune to non-idealities of synaptic devices. The number of write operations on memristive devices is reduced by two orders of magnitude. The recognition accuracy of 95%~97% can be achieved for the MNIST dataset using pulsed synaptic behaviors of various memristive synaptic devices.

eess.SP↗