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Simulation and machine learning based analytical study of single electron transistor (SET)

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Abstract

In recent years, the requirement for greater scalability of transistor technology for the continuation of Moore’s law has led researchers toward the investigations of several innovative advanced semiconductor device as potentially superior alternatives to conventional transistors. Among them, single-electron transistors (SETs) have shown considerable promise in terms of performance and reliability with significant device dimension scaling. However, realistic modeling and simulation are the primary steps toward the practical implementation of SET designs. In this work, a technology computer-aided design simulation-based analytical study of silicon quantum dot SETs is developed to improve the electrical characteristics of the devices through optimization of different device parameters. Further, the investigation is extended to explore the temperature dependency of quantum tunneling by analysis of the characteristic plots of such quantum dot-based nano-devices. Moreover, a machine learning (ML)-based approach has been implemented and validated through development and testing of ML models predicting SET device performance by examining dependence of relevant design parameters on device performance. Hence, the proposed model of SETs provides the analytical understanding for a sustainable and realistic design of SETs allowing approaches to future nano-device-based IC design.

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Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to security reason but are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank Techno International New Town for pursuing the research work. The authors also acknowledge the Department of Electronics and Communication Engineering in the context of provisioning of infrastructure necessary to carry out this work.

Funding

The authors have no relevant financial or non-financial interests to disclose.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Techno International New Town, Kolkata, India

    Jeet Chatterjee, Jenifa Khatun,  Siddhi, Ankit Kumar & Sandip Bhattacharya

  2. Cisma Consultants Private Limited, Whitefield, Bengaluru, India

    Koushik Ghosh

  3. Heartwatch, Bangalore, India

    Judhajit Sanyal

Authors
  1. Jeet Chatterjee
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by JC, JK, AK and S. The simulations were performed by KG, SB and JS. The first draft of the manuscript was written by SB and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Sandip Bhattacharya.

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Chatterjee, J., Khatun, J., Siddhi et al. Simulation and machine learning based analytical study of single electron transistor (SET). J Comput Electron (2024). https://doi.org/10.1007/s10825-024-02175-4

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