AI-Driven R&D Cost Reduction in Semiconductor Design: A Silvaco Case Study of Advanced Si:Sb-Bi/Al₂O₃ Devices

DOI

10.2991/978-94-6239-711-8_33How to use a DOI?

Keywords

AI-Augmented Simulation; Silvaco TCAD; Si:Sb-Bi/Al2O3 Devices; R&D Cost Optimization; Heavy Doping Engineering; Semiconductor Manufacturing

Abstract

This work reports a computational optimization study of heavily doped (10⁻³) Si:Sb–Bi/Al₂O₃ heterostructures using AI-enhanced Silvaco TCAD simulations to address key material design trade-offs. A central challenge in this system arises from the inverse relationship between beneficial lattice strain and electronic transport performance. Baseline simulations indicate that although full bismuth incorporation (x = 1.0) produces a maximum compressive strain of 0.70%, it also causes a severe degradation in carrier transport, reducing the effective electron mobility to 80 cm² V⁻¹ s⁻¹ and increasing the interface trap density (D_it) to 2.5 × 10¹² cm⁻² eV⁻¹ as a result of pronounced ionized impurity scattering and defect clustering. To overcome this limitation without extensive experimental trial-and-error, a multivariable machine-learning optimization framework was embedded directly into the TCAD simulation flow. The AI-driven analysis identified an optimal stoichiometric “sweet spot” at a bismuth fraction of approximately x ≈ 0.35. This configuration achieved a balanced trade-off, restoring the carrier mobility to 112 cm² V⁻¹ s⁻¹ (a 40% improvement over the fully doped case), while maintaining a high active carrier concentration of 9.1 × 10¹⁹ cm⁻³ and suppressing inter-face defect densities to a manageable level of 5.8 × 10¹¹ cm⁻² eV⁻¹. From a tech-no-economic standpoint, the proposed simulation-to-fabrication methodology significantly improves development efficiency. The AI-guided workflow effectively replaces approximately thirteen iterative experimental fabrication cycles, leading to an estimated 66% reduction in R&D time-to-market and an overall development cost reduction of about 75%, thereby demonstrating the strong potential of this approach for cost-effective and competitive semiconductor device design.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Azzeddine Charef
AU  - Amira Sbaihi
AU  - Chaima Benbrika
AU  - Hamza Trir
AU  - Yacine Aoun
AU  - Said Benramache
AU  - Khattra Mimouni
PY  - 2026
DA  - 2026/06/24
TI  - AI-Driven R&D Cost Reduction in Semiconductor Design: A Silvaco Case Study of Advanced Si:Sb-Bi/Al₂O₃ Devices
BT  - Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)
PB  - Atlantis Press
SP  - 356
EP  - 364
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-711-8_33
DO  - 10.2991/978-94-6239-711-8_33
ID  - Charef2026
ER  -