(Invited) Engineering Chalcogenide Materials – From Bulk Optics to CMOS-Compatible Microelectronic Integration
Notice bibliographique
Résumé
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 Ge 23 Sb 7 S 70 films”, Optics Express, 16 (2008) 20081- 20098
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».