Impact of Miniaturization on the Self-Heating Effect in MOSFET Devices

Abstract

With further scaling of MOSFETs to nanometer sizes, thermal effects have been a major issue affecting device performance and reliability. In this paper, we examine the temperature impacts in scaled MOSFETs, with a focus on self-heating mechanisms, thermal resistance modeling, and their effects on circuit performance. Based on an extensive literature survey and electro-thermal simulations, we examine the interplay among scaling, heat dissipation, and degradation of critical electrical parameters. We offer mathematical models for predicting thermal behavior in nanometer-scale devices and propose possible design considerations for avoiding self-heating.

Introduction

The continuous scaling of MOSFETs to sub-10 nm nodes has significantly advanced electronic technologies, enhancing performance and integration. However, this miniaturization introduces critical thermal challenges, primarily due to the self-heating effect (SHE), which arises from increased power densities and reduced heat dissipation areas.

At these nanoscale dimensions, traditional assumptions about heat spreading become invalid. Modern MOSFETs incorporate materials like high-κ dielectrics and low-k insulators, which possess low thermal conductivity, exacerbating heat buildup within the device. This localized temperature rise adversely affects key electrical characteristics such as carrier mobility, threshold voltage, and subthreshold swing, leading to performance degradation and reduced device lifespan.

Advanced transistor architectures, including FinFETs, Gate-All-Around FETs (GAA-FETs), and Silicon-on-Insulator (SOI) devices, face intensified thermal management issues due to their geometric confinement and insulating layers. The study employs analytical modeling, TCAD-based simulations, and literature analysis to investigate the mechanisms underlying SHE and its impact on scaled MOSFETs. The objective is to understand how device scaling influences thermal behavior and to propose strategies—ranging from material selection to structural optimization—to mitigate thermal constraints in next-generation MOSFETs.

Key Objectives:

1. Introduce the physics of self-heating in MOSFETs.

2. Theoretically determine thermal resistance and power dissipation parameters.

3. Model temperature-dependent degradation of significant electrical parameters.

4. Compare analytical models with experimental results and TCAD simulations.

5. Recommend mitigation strategies at material, device, and circuit levels.

Methodology:

The research adopts a multi-faceted approach combining analytical modeling, literature review, and numerical simulations. Key equations utilized include Fourier’s Law for heat conduction, thermal resistance calculations, and mobility degradation models due to elevated temperatures. TCAD simulations are performed using software like Silvaco Atlas and Synopsys Sentaurus to simulate various device structures and operating conditions. These simulations are compared with experimental data to validate model accuracy and assess the impact of SHE on device performance.

Results and Analysis:

• Simulation Findings: Simulations indicate a significant increase in SHE with aggressive device scaling. For instance, in SOI and FinFET structures with gate lengths around 10 nm, peak lattice temperatures rise by 10–50 K under moderate biasing conditions. Devices with thin buried oxides exhibit higher thermal confinement, highlighting the role of oxide engineering in thermal performance.

• Analytical Correlation: Thermal resistance models confirm that narrower and longer channels exhibit higher resistance to heat flow, leading to elevated operating temperatures even with small increases in power dissipation. Mobility degradation models reveal that for every 10 K rise in temperature, carrier mobility decreases by approximately 4–10%, depending on doping levels and substrate material.

• Literature Comparison: The results align with recent studies, such as those by Zhang et al. and Ghibaudo et al., which report similar temperature increases and non-linear behavior of temperature rise with increasing power dissipation.

• Architecture-Specific Trends: Bulk CMOS devices show improved thermal dissipation through direct substrate contact, leading to reduced junction temperatures. In contrast, SOI devices experience thermal isolation due to the buried oxide layer, resulting in increased peak temperatures. FinFETs and GAA-FETs exhibit steep thermal gradients due to dense geometry and constrained heat-spreading channels, making them susceptible to thermal degradation.

Discussion:

• Geometry and Material Impact: The degree of self-heating is significantly influenced by device geometry and material properties. Devices with shorter gate lengths and smaller cross-sectional areas have higher thermal resistance, inhibiting efficient heat dissipation. Materials like high-κ dielectrics and buried oxide layers in SOI technology, while beneficial for electrostatic control, act as thermal barriers, restricting vertical heat conduction.

• Architectural Considerations: While bulk MOSFETs offer better thermal conduction, they face challenges in electrostatic control at advanced nodes. SOI, FinFET, and GAA-FET architectures, though effective in mitigating short-channel effects, are hindered by localized heating and limited heat-spreading paths. Design optimizations, such as higher fin pitch in FinFETs, thermal vias in GAA-FETs, and thinner buried oxide layers in SOI devices, can enhance thermal dissipation without compromising electrical performance.

• System-Level and Circuit-Level Implications: Beyond device-level changes, circuit designers must incorporate thermal-aware design techniques. Methods like dynamic voltage and frequency scaling (DVFS), thermal-aware placement and routing, and on-chip thermal sensing can manage heat in real-time. Software-based techniques, such as load balancing, thermal throttling, and activity migration, can be implemented at the system-on-chip (SoC) level for predictive thermal management. Additionally, the increasing trend of 3D IC stacking introduces new thermal challenges, necessitating solutions like microfluidic cooling, embedded heat sinks, or thermoelectric coolers to maintain temperature uniformity.

• Modeling Challenges and Future Directions: Existing compact models often assume isothermal conditions, which are no longer valid for sub-10 nm devices. There is a growing need to incorporate temperature-dependent parameters in SPICE models and Verilog-A device definitions to accurately represent SHE effects. Integrating machine learning algorithms with TCAD outputs could enable dynamic prediction of thermal behavior, allowing designers to optimize both electrical and thermal performance in next-generation electronics.

Conclusion

This study provides a comprehensive analysis of the self-heating effect (SHE) in miniaturized MOSFET devices, highlighting how device scaling and advanced architectures have amplified thermal challenges. Through analytical modeling and TCAD simulations, we have demonstrated that localized heating significantly impacts key device metrics—such as carrier mobility, threshold voltage, and subthreshold swing—thereby affecting both performance and long-term reliability. Our findings reveal that as gate lengths shrink and integration densities rise, the thermal resistance of transistors increases, especially in architectures like SOI, FinFETs, and GAA-FETs. These structures, while beneficial for electrostatic control, suffer from limited heat dissipation pathways. The results emphasize that material selection, structural design, and architectural choices must collectively address thermal constraints to ensure stable device operation. To mitigate the adverse effects of self-heating, we have discussed strategies at multiple levels: 1) Material level: Introducing substrates with superior thermal conductivity. 2) Device level: Optimizing fin geometry, oxide thickness, and incorporating thermal vias. 3) Circuit and system level: Employing dynamic thermal management, layout optimization, and real-time temperature monitoring. Furthermore, the study recommends the evolution of compact models to include temperature-dependent parameters, ensuring accurate prediction of device behaviour under realistic operating conditions. Future research may also explore the integration of machine learning with electrothermal simulations for predictive thermal modeling and smarter thermal-aware design tools. As technology nodes continue to shrink and performance demands grow, addressing self-heating will be crucial to sustaining reliability, efficiency, and scalability in the next generation of semiconductor devices.

References

[1] X. Huang et al., \"Self-Heating Effects in Gate-all-around Silicon Nanowire MOSFETs: Modeling and Analysis,\" ISQED, 2012. [2] M. A. Mastro et al., \"Design of Ga2O3 Modulation Doped Field Effect Transistors,\" JVST A, 2021. [3] P. A. \'t Hart et al., \"Self-Heating in Nanometer Bulk-CMOS at Cryogenic Temperatures,\" arXiv:2106.07982. [4] G. Ghibaudo et al., \"Modeling of Self-Heating in FDSOI MOSFETs,\" arXiv:2309.04199. [5] X. Zhang, \"Measurements and Simulations of Self-Heating in 40nm SOI MOSFETs,\" PhD Diss., ASU, 2020. [6] A. E. Atamuratov et al., \"Self-Heating Effect in SOI Junction less FinFETs,\" IEEE CDE, 2021. [7] F. Nasri et al., \"Investigation of Self-Heating Effects in 10nm SOI MOSFETs,\" IEEE Trans. Electron Devices. [8] F. J. de Souza et al., \"Simulation of Self-Heating in SOI nMOSFETs,\" Centro Universitário da FEI, Brazil. [9] S. Rusu et al., “Advanced 3D Packaging with Thermal-Aware Design,” IEEE Design & Test of Computers. [10] Y. Taur and T. H. Ning, \"Fundamentals of Modern VLSI Devices,\" Cambridge University Press. [11] A. Majumdar, “Microscale Heat Conduction in Dielectric Thin Films,” Journal of Heat Transfer, vol. 115, no. 1, pp. 7–16, 1993. [12] T. Grasser et al., “Analytical Modeling of Temperature Effects on Mobility and Reliability,” IEEE Trans. Electron Devices, vol. 58, no. 11, pp. 3845–3852, 2011. [13] C. Jungemann and B. Meinerzhagen, “Hierarchical Device Simulation Using the Hydrodynamic Model,” Springer, 2003. [14] K. Banerjee et al., “3D ICs: A Novel Chip Design for Improving Deep-Submicron Interconnect Performance and Systems-on-Chip Integration,” Proc. IEEE, vol. 89, no. 5, pp. 602–633, May 2001. [15] S. L. Wong et al., “CMOS Scaling into the Nanometer Regime: Physics, Modeling, and Design Considerations,” Proc. IEEE, vol. 91, no. 11, pp. 1805–1820, 2003. [16] H. Iwai, “Roadmap for 22 nm and Beyond,” Microelectronics Engineering, vol. 86, no. 7–9, pp. 1520–1528, 2009 [17] A. Islam and K. Roy, “Modeling of Self-Heating and Channel Temperature in Sub-100nm SOI MOSFETs,” IEEE Trans. Nanotechnology, vol. 6, no. 1, pp. 79–90, 2007. [18] D. Esseni and P. Palestri, “Physics-Based Modeling and Simulation of Nanoscale Transistors,” Cambridge University Press, 2011. [19] M. Lundstrom, “Fundamentals of Carrier Transport,” Cambridge University Press, 2000 [20] E. Pop, “Energy Dissipation and Transport in Nanoscale Devices,” Nano Research, vol. 3, no. 3, pp. 147–169, 2010.

Copyright

Copyright © 2025 M L Sharma, Sunil Kumar, Ajay Kumar Garg, Aryan Mishra, Chirag Vohra, Omprakash Barik, Vipin Sharma, Meet Saini. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.