(Invited) Engineering Chalcogenide Materials – From Bulk Optics to CMOS-Compatible Microelectronic Integration
Bibliographic record
Abstract
Optical sensor technologies for chemical detection have continued to advance in recent years based on extensive research to enhance device sensitivity, specificity and accuracy. To overcome the large footprint and high cost issues of traditional spectroscopic sensing technologies such as FTIR (Fourier Transform InfraRed) and Raman spectroscopy, several novel sensing technologies have been developed that envision low cost, miniaturized platforms for chemical detection, including evanescent fiber/waveguide sensors, SERS (surface enhanced Raman scattering) [1] and SPR (surface plasmonic resonance)/LSPR sensors [2]. Such next generation optical and opto-electronic components will require materials that possess unique, spectrally agile, multi-functional attributes that can be produced via low(er) cost manufacturing processes. Material compositional design and novel processing and fabrication strategies will be essential to the success in realizing new materials that fit application-specific needs. Efforts by our team have focused on use of IR transmissive glasses in planar form on Si, which lend themselves to integration with an on-chip source, semiconductor detector. Such devices exploit the enhanced sensitivity that comes from using probe light in the mid-infrared region (MIR) where these materials have higher response that overlap with fundamental molecular fingerprints of target analytes. While prior efforts by our team have largely focused on designing glasses for bulk, fiber and planar infrared optical applications, the use of chalcogenide glass, glass ceramics and other alloys are broadly attractive due to their diverse thermal, mechanical, and semi-conducting attributes and are most recently finding their way into the ‘photonic material toolbox’ that exploits other uses including phase change (PCM) or high mobility materials for emerging electronics applications. This presentation aims to highlight specific examples of such material design strategies for optical and electronic application areas, which have guided our ability to compositionally optimize infrared chalcogenide alloys, for a diverse range of applications. Discussed are the results of efforts to modify material chemistry choices to more closely align with material manufacturing techniques that are CMOS-fabrication compatible and, ultimate component or device performance. The discussion will be illustrated with the recent results from our team [3] capitalize on ultra-high-Q optical resonance to enable sensitive detection of small optical property perturbations (optical absorption and/or refractive index change) associated with strong photon-molecule interaction with the target species of interest and resonant enhancement to boost sensitivity for the development of planar, optical sensors. References [1] “Advances in chalcogenide fiber evanescent wave biochemical sensing,” P. Lucas et al., Analytical Biochemistry 351, 1-10 (2006) [2] “High-performance sensor based on surface plasmon resonance with chalcogenide prism and aluminum for detection in infrared,” R. Jha et al., Opt. Lett. 34, 749-751 (2009) [3] “Integrated chalcogenide waveguide resonators for mid-IR sensing: Leveraging material properties to meet fabrication challenges,” N. Carlie et al., Opt. Express 18 26728-26743 (2010) [4] “Towards universal enrichment nanocoating for IR-ATR waveguides,” J. Giammarco, B. Zdyrko, L. Petit, J. D. Musgraves, J. Hu, A. Agarwal, L. Kimerling, K. Richardson, and I. Luzinov, Chemical Communications 47 32 (2011) 9104-9106 [5] C. Lopez, (2004) “Evaluation of the Photo-Induced Structural Mechanisms in Chalcogenide Glass”, Ph.D; Thesis, College of Optics and Photonics at the University of Central Florida [6] T. Anderson et al, “Evaluation of the femtosecond laser photo-response of Ge23Sb7S70 films”, Optics Express, 16 (2008) 20081- 20098
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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 source (direct Gemma or distilled Codex), 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".