arXiv subjects

Jingxian Li

Publications and source records attributed to Jingxian Li.

5 recordsLinked to original sources

arXiv2026

Achieving Long Retention in Area-Dependent Resistive Memory with Phase-Separated Amorphous Tantalum Oxide

Resistive random-access memory (ReRAM) is a promising future nonvolatile memory technology. Most ReRAM exhibit a fundamental tradeoff: filament-type ReRAM provides long data retention but suffers from poor uniformity and high switching current, whereas nonfilamentary ReRAM shows lower-current, area-dependent switching but generally poor retention. No two-terminal device has been able to overcome this tradeoff. In this work, we present a two-terminal Ta2O5/ TaOX resistive memory cell that achieves both nonfilamentary switching and long retention. Electrical measurements and composition depth profile show that the switching is not confined to a single filament but is instead uniform across the entire switching region. Despite the nonfilamentary switching, this device can retain information for over 22 hours at 190 °C, which is comparable to the best filamentary devices. We propose that this long retention arises from composition phase separation in amorphous tantalum oxide. Our work shows that the fundamental tradeoff between information retention and switching uniformity can be overcome, and thereby provides a pathway toward more uniform and reliable oxide memory devices.

physics.app-ph↗

arXiv2024

Nonvolatile Electrochemical Memory at 600C Enabled by Composition Phase Separation

CMOS-based microelectronics are limited to ~150°C and therefore not suitable for the extreme high temperatures in aerospace, energy, and space applications. While wide bandgap semiconductors can provide high-temperature logic, nonvolatile memory devices at high temperatures have been challenging. In this work, we develop a nonvolatile electrochemical memory cell that stores and retains analog and digital information at temperatures as high as 600 °C. Through correlative electron microscopy, we show that this high-temperature information retention is a result of composition phase separation between the oxidized and reduced forms of amorphous tantalum oxide. This result demonstrates a memory concept that is resilient at extreme temperatures and reveals phase separation as the principal mechanism that enables nonvolatile information storage in these electrochemical memory cells.

physics.app-ph↗

arXiv2023

MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging

In medical image analysis, transfer learning is a powerful method for deep neural networks (DNNs) to generalize well on limited medical data. Prior efforts have focused on developing pre-training algorithms on domains such as lung ultrasound, chest X-ray, and liver CT to bridge domain gaps. However, we find that model fine-tuning also plays a crucial role in adapting medical knowledge to target tasks. The common fine-tuning method is manually picking transferable layers (e.g., the last few layers) to update, which is labor-expensive. In this work, we propose a meta-learning-based LR tuner, named MetaLR, to make different layers automatically co-adapt to downstream tasks based on their transferabilities across domains. MetaLR learns appropriate LRs for different layers in an online manner, preventing highly transferable layers from forgetting their medical representation abilities and driving less transferable layers to adapt actively to new domains. Extensive experiments on various medical applications show that MetaLR outperforms previous state-of-the-art (SOTA) fine-tuning strategies. Codes are released.

cs.CV↗

arXiv2022

Rethinking Two Consensuses of the Transferability in Deep Learning

Deep transfer learning (DTL) has formed a long-term quest toward enabling deep neural networks (DNNs) to reuse historical experiences as efficiently as humans. This ability is named knowledge transferability. A commonly used paradigm for DTL is firstly learning general knowledge (pre-training) and then reusing (fine-tuning) them for a specific target task. There are two consensuses of transferability of pre-trained DNNs: (1) a larger domain gap between pre-training and downstream data brings lower transferability; (2) the transferability gradually decreases from lower layers (near input) to higher layers (near output). However, these consensuses were basically drawn from the experiments based on natural images, which limits their scope of application. This work aims to study and complement them from a broader perspective by proposing a method to measure the transferability of pre-trained DNN parameters. Our experiments on twelve diverse image classification datasets get similar conclusions to the previous consensuses. More importantly, two new findings are presented, i.e., (1) in addition to the domain gap, a larger data amount and huge dataset diversity of downstream target task also prohibit the transferability; (2) although the lower layers learn basic image features, they are usually not the most transferable layers due to their domain sensitivity.

cs.CV↗

arXiv2022

Nonvolatile Electrochemical Random-Access Memory Under Short Circuit

Electrochemical random-access memory (ECRAM) is a recently developed and highly promising analog resistive memory element for in-memory computing. One longstanding challenge of ECRAM is attaining retention time beyond a few hours. This short retention has precluded ECRAM from being considered for inference classification in deep neural networks, which is likely the largest opportunity for in-memory computing. In this work, we develop an ECRAM cell with orders of magnitude longer retention than previously achieved, and which we anticipate to exceed 10 years at 85C. We hypothesize that the origin of this exceptional retention is phase separation, which enables the formation of multiple effectively equilibrium resistance states. This work highlights the promises and opportunities to use phase separation to yield ECRAM cells with exceptionally long, and potentially permanent, retention times.

cs.ET↗