Stress Engineering of Dielectric Films on Semiconductor Substrates
Bibliographic record
Abstract
Dielectric thin films deposited by plasma enhanced chemical vapor deposition (PECVD) have been extensively studied over the last decades due to their interesting optical and electrical properties besides their many applications in microelectronic and optoelectronic devices. Recently published studies have shown the impact of the mechanical properties of amorphous dielectric films on semiconductor substrates [1]. In strain engineering, stressed films are used to control on demand the physical properties of semiconductors at the surface such as bandgap energy, dielectric constant, and refractive index. We aim to study in this work how to control the distribution of the strain field beneath a dielectric film and how the changing of the residual stress affects the physical properties of the dielectric film itself. We deposited hydrogenated amorphous silicon nitride a-SiN:H films on Si, InP, and GaAs substrates using a capacitively coupled plasma reactor CCP-PECVD with a radiofrequency (RF) power at 13.56 MHz.The a-SiN:H films were deposited at 280 °C, with a thickness of approximatively 500 nm, using a SiH4/NH3/N2/Ar precursor mixture. The RF power injected into the plasma allows a tunable residual stress and a wide range of built-in stress, from tensile (+ 300 MPa) to compressive (– 400 MPa). To evaluate the residual stress in our deposited thin films, we used the standard method of wafer curvature measurements. The thickness and the refractive index were characterized by variable angle spectroscopic ellipsometry (VASE). The determination of Young’s modulus and hardness of the a-SiN:H films was performed by nanoindentation. We noticed that the adjustment of the residual stress leads to the modification of the film in terms of optical and mechanical properties. In order to investigate the deformation induced in the semiconductor, an understanding of the semiconductor mechanical behavior on a microscopic scale is required. Thus, we performed a detailed investigation of the effect of strain on the degree of polarization (DOP) of the photoluminescence signal on direct bandgap substrates [2]. After examining the DOP profiles beneath the film, it is interesting to note that the anisotropic deformation extends to significant depths (~ 8 µm), as illustrated in figure 1, while the horizontal distribution of the stress can propagate beyond the edge of the sample by a few microns (see figure 2). The confinement of light in some photonic devices such as photoelastic planar waveguides can be achieved by a photo-elastic effect in semiconductor using stressed dielectric films [3]. [1] S. Gérard et al., “Photoluminescence mapping of the strain induced in InP and GaAs substrates by SiNx stripes etched from thin films grown under controlled mechanical stress,” Thin Solid Films, vol. 706, p. 138079, Jul. 2020, doi: 10.1016/j.tsf.2020.138079. [2] D. T. Cassidy, C. K. Hall, O. Rehioui, and L. Bechou, “Strain estimation in III-V materials by analysis of the degree of polarization of luminescence,” Microelectron. Reliab., vol. 50, no. 4, pp. 462–466, Apr. 2010, doi: 10.1016/j.microrel.2009.11.003. [3] P. A. Kirkby, P. R. Selway, and L. D. Westbrook, “Photoelastic waveguides and their effect on stripe-geometry GaAs/Ga 1-xAlxAs lasers,” J. Appl. Phys., vol. 50, no. 7, pp. 4567–4579, Jul. 1979, doi: 10.1063/1.326563. Figure 1
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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 | 0.000 | 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) | 0.001 | 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 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".