(Invited) Challenges of Energy Band Engineering with New Sn-Related Group IV Semiconductor Materials for Future Integrated Circuits
Bibliographic record
Abstract
Research and development of GeSn and related group-IV semiconductor materials have been widely extended in recent years for not only electronic transistors but also various optoelectronic applications [1,2]. Design and engineering of the energy band structure of group-IV semiconductor materials are swiftly blossoming with establishing the crystal growth technology of GeSn and related materials. We have successively developed the crystalline growth technology of GeSn and GeSiSn thin films on various substrates mainly by using molecular beam epitaxy [1-7]. Recently, we also achieved the epitaxial growth of GeSn layer by using metal organic chemical vapor deposition method [8]. In this presentation, we will report our recent achievements of our study for the crystalline growth and electronic properties including energy band structure of GeSn and related group-IV materials. Controlling the composition of elements and the strain structure of the group-IV semiconductor alloys promises prospective energy band engineering technology. Increasing in the Sn content over about 10% or tensile strain over about 1% achieve the indirect-to-direct crossover, which is a strong driving force of the optical applications of GeSn material. In addition, we recently achieved the formation of polycrystalline SiSn thin layers with a very high Sn content of 20%, and demonstrated that the direct bandgap decreases to 1.05 eV with Si-Sn alloying, which is just 0.22 eV higher than the indirect bandgap of the poly-SiSn even by using Si matrix material [9]. Also, the ternary alloy GeSiSn promises the control of the energy band structure independently on the lattice constant by changing each content of three elements. We found that unstrained GeSiSn/Ge heterostructure realizes a type-I energy band alignment without using strain structure, that promises various electronic and optoelectronic applications [10]. We will demonstrate the experimental results of the energy band engineering with GeSn and related materials. The energy band engineering also provides controlling technology of the electronic property at the interface such as metal/Ge contact. Recently, we found that the Sn/Ge or GeSn/Ge contacts effectively reduces the Schottky barrier height at the metal/n-Ge interface [11,12]. In our presentation, we will also discuss the influence of the energy band structure on the interface properties for Ge and GeSn. This work was partially supported by Grant-in-Aid for Scientific Researches of the JSPS and the JSPS Core-to-Core Program, A. Advanced Research Networks. References [1] S. Zaima, Jpn. J. Appl. Phys. 52, 030001 (2013). [2] S. Zaima et al., Sci. Technol. Adv. Mater., submitted. [3] S. Zaima et al., ECS Trans. 41, 231 (2011). [4] S. Zaima et al., ECS Trans. 50, 897 (2012). [5] O. Nakatsuka et al., ECS Trans. 58, 149 (2013). [6] S. Zaima et al. ECS Trans. 64, 147 (2014). [7] O. Nakatsuka et al. ECS Trans. 64, 793 (2014). [8] Y. Inuzuka et al., ECS Solid State Lett., submitted. [9] M. Kurosawa et al., Appl. Phys. Lett. 106, 171908 (2015). [10] T. Yamaha et al., in Abstr. of ICSI-9, Montreal, Canada, May, 2015. [11] Suzuki et al., Jpn. J. Appl. Phys. 53, 04EA06 (2014). [12] A. Suzuki et al., SSDM2015, Hokkaido, Japan, Sept. 2015, to be submitted.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.014 |
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".