2019
DOI: 10.1007/s13738-018-01574-2
|View full text |Cite
|
Sign up to set email alerts
|

Modeling and optimization of characterization of nanostructure anodized aluminium oxide membranes

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
4
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
3
3

Relationship

0
6

Authors

Journals

citations
Cited by 6 publications
(4 citation statements)
references
References 48 publications
0
4
0
Order By: Relevance
“…AAO gradually grows in acidic solutions that anodize aluminum under an electric field between the anode and the cathode. The diameter of AAO pores depends on the anodization potential (voltage), temperature, electrolyte, and concentration [29,30]. The pore diameter and the distance between the pores often increase as the anodization voltage or the electrolyte concentration increases [14,31,32].…”
Section: Introductionmentioning
confidence: 99%
“…AAO gradually grows in acidic solutions that anodize aluminum under an electric field between the anode and the cathode. The diameter of AAO pores depends on the anodization potential (voltage), temperature, electrolyte, and concentration [29,30]. The pore diameter and the distance between the pores often increase as the anodization voltage or the electrolyte concentration increases [14,31,32].…”
Section: Introductionmentioning
confidence: 99%
“…Taking chip design for example, we need to iteratively select chip cells from a pool of cells and place them in appropriate positions onto a chip canvas to obtain a chip of high quality in the end as described by Mirhoseini et al (2020). Current practises in these areas have relied heavily on human domain expertise, as a result, these approaches could be expensive (i.e., requiring highly specialized skills), inefficient (i.e., throughput bottlenecked by the availability of human experts as well as their latency), ineffective (i.e., quality depends entirely on the expertise level of human designers and is often hard to control or to further optimize), and difficult to scale (i.e., solutions are often case specific, not easily transferable or automatable) as done in works (Liwo et al, 1999;Azami and Omidkhah, 2019;Kalinli, 2004;Kool et al, 2019). The recent work of Vinyals et al (2015a) suggests a promising new direction of using data-driven approach to tackle this challenging problem automatically using machine learning.…”
Section: Introductionmentioning
confidence: 99%
“…
This article describes the research and development of nanoporous anodic aluminum oxide (AAO) membrane fabrication technology. There are many studies in the literature that analyse the dependence of the pore geometry of AAO on various parameters such as anodization time, temperature, type of electrolyte, concentration, etc [1][2][3][4]. However, control of the geometry of AAO nanopores using high-frequency excitation techniques during the anodization process is in the early stages of research, so no dependence has been established yet.
…”
mentioning
confidence: 99%