Chien-Hua Hsu

Dr. Chien-Hua Hsu

orcid

Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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13 June 2017 in Discover Nano
Verified
Journal Article
Open access
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YLYu-De Lin
Yu-De Lin
PCPang-Shiu Chen
Pang-Shiu Chen
HLHeng-Yuan Lee
Heng-Yuan Lee
YCYu-Sheng Chen
Yu-Sheng Chen
SRSk. Ziaur Rahaman
Sk. Ziaur Rahaman
CLChrong Jung Lin
Chrong Jung Lin
A retention behavior model for self-rectifying TaO/HfO x - and TaO/AlO x -based resistive random-access memory (RRAM) is proposed. Trapping-type RRAM can have a high resistance state (HRS) and a low resistance state (LRS); the degradation in a LRS is usually more severe than that in a HRS, because the LRS during the SET process is limited by the internal resistor layer. However, if TaO/AlO x elements are stacked in layers, the LRS retention can be improved. The LRS retention time estimated by extrapolation method is more than 5 years at room temperature. Both TaO/HfO x - and TaO/AlO x -based RRAM structures have the same capping layer of TaO, and the activation energy levels of both types of structures are 0.38 eV. Moreover, the additional AlO x switching layer of a TaO/AlO x structure creates a higher O diffusion barrier that can substantially enhance retention, and the TaO/AlO x structure also shows a quite stable LRS under biased conditions.

27 June 2024 in Materials
Verified
Journal Article
Open access
PDF file
CHChien-Hua Hsu
Chien-Hua Hsu
HCHao-Yu Chan
Hao-Yu Chan
MCMing-Hui Chang
Ming-Hui Chang
CLChiung-Fang Liu
Chiung-Fang Liu
TLTzu-Yu Liu
Tzu-Yu Liu
KCKuo-Chuang Chiu
Kuo-Chuang Chiu
This study explores the integration of machine learning (ML) techniques to predict and optimize the compressive strength of alkali-activated materials (AAMs) sourced from four industrial waste streams: blast furnace slag, fly ash, reducing slag, and waste glass. Aimed at mitigating the labor-intensive trial-and-error method in AAM formulation, ML models can predict the compressive strength and then streamline the mixture compositions. By leveraging a dataset of only 42 samples, the Random Forest (RF) model underwent fivefold cross-validation to ensure reliability. Despite challenges posed by the limited datasets, meticulous data processing steps facilitated the identification of pivotal features that influence compressive strength. Substantial enhancement in predicting compressive strength was achieved with the RF model, improving the model accuracy from 0.05 to 0.62. Experimental validation further confirmed the ML model’s efficacy, as the formulations ultimately achieved the desired strength threshold, with a significant 59.65% improvement over the initial experiments. Additionally, the fact that the recommended formulations using ML methods only required about 5 min underscores the transformative potential of ML in reshaping AAM design paradigms and expediting the development process.