ReviewiScience2025
Optical multilayer thin film structure inverse design: From optimization to deep learning.
Review in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Bayesian Optimization-Driven Design of Hydrogen Concentration in A-Si/H Multilayers for Silicon Heterojunction Solar Cells.ACS applied materials & interfaces · 2026Article
- Engineering of Optoelectronic Devices for Renewable Energy Applications.Micromachines · 2026Review
- Accurate Inverse Design of Broadband Solar Metamaterial Absorbers via Joint Forward-Inverse Deep Learning.Nanomaterials (Basel, Switzerland) · 2026Article
- A surrogate-based inverse design framework for targeted diameter control of electrospun nanofibers.Scientific reports · 2026Article
- Sensitivity Analysis of Process Parameters on Deposition Quality and Multi-Objective Prediction in Ion-Assisted Electron Beam Evaporation of TaMicromachines · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optical multilayer thin film structures have been widely used in numerous photonic domains and applications. The key component to enable these applications is the inverse design. Different from other photonic structures such as metasurface or waveguide, multilayer thin film is a one-dimensional structure, which deserves its own treatment for the design process. Optimization has always been the standard design algorithm for decades. Recent years have witnessed a rapid development of integrating different deep learning algorithms to tackle the inverse design problems. A natural question to ask is: how do these algorithms differ from each other? Why do we need to develop so many algorithms and what type of challenges do they solve? What is the state-of-the-art algorithm in this domain? Here, we review recent progress and provide a guide tour through this research area, starting from traditional optimization to recent deep learning approaches. Challenges and future perspectives are also discussed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.