Deep Learning-Based Thermal Prediction Models for Solid-State Electronic Devices

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 04 | Issue : 01 | Page :
    By

    Rosalin Pradhan,

  • Bibhu Prasad Ganthia,

  1. Assistant Professor, Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India
  2. Assistant Professor, Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India

Abstract

The rapid advancement of solid-state electronic devices in high-performance computing, communication systems, automotive electronics, and renewable energy applications has significantly increased concerns related to thermal management and device reliability. Excessive heat generation in semiconductor devices adversely affects operational efficiency, switching performance, lifespan, and overall system stability. Traditional thermal prediction methods often require complex numerical computations and extensive simulation time, making them less suitable for real-time monitoring and adaptive control applications. This study proposes a deep learning-based thermal prediction model for solid-state electronic devices using advanced neural network architectures to accurately estimate device temperature behavior under varying operating conditions. The proposed framework integrates parameters such as power dissipation, ambient temperature, switching frequency, load current, and material properties to develop an intelligent predictive system. A hybrid Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) model is employed to capture both temporal and spatial thermal characteristics of semiconductor devices. Simulation and experimental analyses demonstrate that the proposed model achieves high prediction accuracy with reduced computational complexity compared to conventional thermal estimation techniques. The developed approach improves thermal stability analysis, enhances device reliability, and supports predictive maintenance in advanced electronic systems. Furthermore, the proposed methodology can be integrated into smart thermal management systems for next- generation VLSI circuits, power electronics, and embedded solid-state applications. The study highlights the growing importance of artificial intelligence-driven thermal prediction models in the design and optimization of modern electronic devices.

Keywords: Deep Learning; Thermal Prediction; Solid-State Electronic Devices; CNN-LSTM Model; Semiconductor Devices; Thermal Management; Artificial Intelligence; Power Electronics; VLSI Systems; Device Reliability; Temperature Estimation; Smart Electronics Systems.

[This article belongs to International Journal of Solid State Innovations & Research ]

How to cite this article:
Rosalin Pradhan, Bibhu Prasad Ganthia. Deep Learning-Based Thermal Prediction Models for Solid-State Electronic Devices. International Journal of Solid State Innovations & Research. 2026; 04(01):-.
How to cite this URL:
Rosalin Pradhan, Bibhu Prasad Ganthia. Deep Learning-Based Thermal Prediction Models for Solid-State Electronic Devices. International Journal of Solid State Innovations & Research. 2026; 04(01):-. Available from: https://journals.stmjournals.com/ijssir/article=2026/view=245369


References

  1. Nan J, Chen J, Li M, Li Y, Ma Y, Fan X. A temperature prediction model for flexible electronic devices based on GA-BP neural network and experimental verification. Micromachines. 2024 Mar 23;15(4):430.
  2. Chuttar A, Banerjee D. Machine learning (ML) based thermal management for cooling of electronics chips by utilizing thermal energy storage (TES) in packaging that leverages phase change materials (PCM). Electronics. 2021 Nov 13;10(22):2785.
  3. Peng Z, He H. Deep Learning-Enhanced Electronic Packaging Defect Detection via Fused Thermal Simulation and Infrared Thermography. Applied Sciences. 2025 Jun 11;15(12):6592.
  4. Benelhaouare AZ, Mellal I, Saydé M, Nicolescu G, Lakhssassi A. Thermal Side-Channel Threats in Densely Integrated Microarchitectures: A Comprehensive Review for Cyber–Physical System Security. Micromachines. 2025 Oct 11;16(10):1152.
  5. Darpan V, Baibhab C. Thermal Management Challenges in 2.5 D and 3D Chiplet Integration: A Review on Architecture–Cooling Co-Design. Eng. 2025;6(12):373.
  6. Stoikos P, Garyfallou D, Floros G, Evmorfopoulos N, Stamoulis G. Fast Electromigration Analysis via Asymmetric Krylov-Based Model Reduction. Electronics. 2025 Jul 8;14(14):2749.
  7. Sun B, Xu Z. Crosstalk analysis of delay-insensitive code in high-speed package interconnects. Micromachines. 2023 May 11;14(5):1033.
  8. Ranjan S, Jaiswal S, Latif A, Das DC, Sinha N, Hussain SS, Ustun TS. Isolated and interconnected multi-area hybrid power systems: A review on control strategies. Energies. 2021 Dec 8;14(24):8276.
  9. Mnejja S, Aydi Y, Abid M, Monteleone S, Catania V, Palesi M, Patti D. Delta multi-stage interconnection networks for scalable wireless on-chip communication. Electronics. 2020 May 30;9(6):913.
  10. Cirstea M, Benkrid K, Dinu A, Ghiriti R, Petreus D. Digital electronic system-on-chip design: Methodologies, tools, evolution, and trends. Micromachines. 2024 Feb 7;15(2):247.
  11. De La Rosa JM. AI-assisted sigma-delta converters—Application to cognitive radio. IEEE Transactions on Circuits and Systems II: Express Briefs. 2022 Mar 23;69(6):2557-63.
  12. Zhao Y, Zou L, Yu B. Physical design for advanced 3D ICs: Challenges and solutions. InProceedings of the 2025 International Symposium on Physical Design 2025 Mar 16 (pp. 209-216).
  13. Wang H, Ma J, Yang Y, Gong M, Wang Q. A review of system-in-package technologies: Application and reliability of advanced packaging. Micromachines. 2023 May 29;14(6):1149.
  14. Lu L, Zhu J, Li Y, Nagarajan A, Liu J, Shiau SY, Ai X. 3D-IC In-Design Thermal Analysis and Optimization. In2024 23rd IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm) 2024 May 28 (pp. 1-5). IEEE.
  15. Pellis S. Golden Fractals in Fluid Dynamics and Turbulence. Available at SSRN 5543579. 2025 Sep 28.
  16. Dash PP, Kazerani M. Harmonic elimination in a multilevel current-source inverter-based grid- connected photovoltaic system. InIECON 2012-38th Annual Conference on IEEE Industrial Electronics Society 2012 Oct 25 (pp. 1001-1006). IEEE.
  17. Xie H, Wang Y, Gao Z, Ganthia BP, Truong CV. Research on frequency parameter detection of frequency shifted track circuit based on nonlinear algorithm. Nonlinear Engineering. 2021 Jan 1;10(1):592-9.
  18. Gu J, Wang W, Yin R, Truong CV, Ganthia BP. Complex circuit simulation and nonlinear characteristics analysis of GaN power switching device. Nonlinear Engineering. 2021 Jan 1;10(1):555- 62.
Regular Issue Subscription Review Article
Volume 04
Issue 01
Received 26/05/2026
Accepted 27/05/2026
Published 27/05/2026
Publication Time 1 Days
1

Login


My IP

PlumX Metrics