Photothermal depth profiling: Comparison between genetic algorithms and thermal wave backscattering (abstract)
Bibliographic record
Abstract
Photothermal depth profiling has been the subject of many papers in the last years. Inverse problems on different kinds of materials have been identified, classified, and solved. A first classification has been done according to the type of depth profile: the physical quantity to be reconstructed is the optical absorption in the problems of type I, the thermal effusivity for type II, and both of them for type III. Another classification may be done depending on the time scale of the pump beam heating (frequency scan, time scan), or on its geometrical symmetry (one- or three-dimensional). In this work we want to discuss two different approaches, the genetic algorithms (GA) [R. Li Voti, C. Melchiorri, C. Sibilia, and M. Bertolotti, Anal. Sci. 17, 410 (2001); R. Li Voti, Proceedings, IV Int. Workshop on Advances in Signal Processing for Non-Destructive Evaluation of Materials, Quebec, August 2001] and the thermal wave backscattering (TWBS) [R. Li Voti, G. L. Liakhou, S. Paoloni, C. Sibilia, and M. Bertolotti, Anal. Sci. 17, 414 (2001); J. C. Krapez and R. Li Voti, Anal. Sci. 17, 417 (2001)], showing their performances and limits of validity for several kinds of photothermal depth profiling problems: The two approaches are based on different mechanisms and exhibit obviously different features. GA may be implemented on the exact heat diffusion equation as follows: one chromosome is associated to each profile. The genetic evolution of the chromosome allows one to find better and better profiles, eventually converging towards the solution of the inverse problem. The main advantage is that GA may be applied to any arbitrary profile, but several disadvantages exist; for example, the complexity of the algorithm, the slow convergence, and consequently the computer time consumed. On the contrary, TWBS uses a simplified theoretical model of heat diffusion in inhomogeneous materials. According to such a model, the photothermal signal depends linearly on the thermal effusivity inhomogeneities, which may be detected because they act as backscattering centers for the heat flux. The physical problem is reduced to the inversion of a algebraic linear system. The advantage is that TWBS allows excellent reconstructions, but only within the limits of validity of the approximate model, which include any slowly varying profile. Recently we have tested the perfomance of both TWBS and GA on linear conductivity profiles. In other words, we have done the numerical simulations of the photothermal measurements coming from a film over a substrate, where the conductivity in the film changes linearly from k1 at the surface, to k2 at the substrate. TWBS and GA have been used to reconstruct the original profiles. If the conductivity mismatch ranges as 0.2<k1/k2<5, the error averaged over the whole profile is lower than 1% for TWBS, and lower than 2% for GA. However, in the case of a stronger conductivity mismatch GA exhibits better performances. These results will be widely discussed in a publication in the near future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".