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Dr. Gowthami Ummadisetti 

Assistant Professor, Department of Electronics and Communication Engineering, School of Engineering, Amaravati

Qualification: Ph.D., M.Tech, B.Tech
u_gowthami@av.amrita.edu
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Bio

Dr. Gowthami Ummadisetti is an Assistant Professor in the Department of Electronics and Communication Engineering, Amrita School of Engineering, Amaravati Campus. She completed her Ph.D. in VLSI from SRM University-AP in 2026, M.Tech in VLSI from VIT University-AP in 2021, and B.Tech in Electronics and Communication Engineering in 2017. She was awarded a Gold Medal for academic excellence during her M.Tech. Prior to her academic career, she gained one year of software industry experience as a Java Developer. Her research focuses on nanoscale semiconductor devices, particularly nanosheet FETs and tree-shaped NSFETs, spacer engineering, temperature-dependent analog/RF performance, dielectric-modulated biosensors, and machine-learning-assisted device modelling. Her research has appeared in IEEE Access, Physica Scripta, Results in Engineering, Silicon, and international conference proceedings. 

Publications

Journal Article

Year : 2026

Comparative Analysis of FinFET and NSFET Architectures for Label-Free Biosensing Applications

Cite this Research Publication : U. Gowthami, Talla Srinivasa Rao, Subhashini Tata, K. Srilakshmi, M. Durga Prakash, Comparative Analysis of FinFET and NSFET Architectures for Label-Free Biosensing Applications, Silicon, Springer Science and Business Media LLC, 2026, https://doi.org/10.1007/s12633-026-03644-0

Publisher : Springer Science and Business Media LLC

Year : 2025

Gate-all-around tree-shaped NSFET-based biosensor: A high-sensitivity approach for label-free biomolecule detection

Cite this Research Publication : U. Gowthami, M. Durga Prakash, B.V.V. Satyanarayana, G. Prasanna Kumar, Asisa Kumar Panigrahy, Amit Krishna Dwivedi, Gate-all-around tree-shaped NSFET-based biosensor: A high-sensitivity approach for label-free biomolecule detection, Results in Engineering, Elsevier BV, 2025, https://doi.org/10.1016/j.rineng.2025.105754

Publisher : Elsevier BV

Year : 2025

Design and temperature analysis of tree-shaped nanosheet FET for analog and RF applications

Cite this Research Publication : Gowthami U, Durga Prakash M, Supraja Patta, V Bharath Sreenivasulu, Design and temperature analysis of tree-shaped nanosheet FET for analog and RF applications, Physica Scripta, IOP Publishing, 2025, https://doi.org/10.1088/1402-4896/ad9cf8

Publisher : IOP Publishing

Year : 2024

Device-Simulation-Based Machine Learning Technique and Performance Optimization of NSFET

Cite this Research Publication : Ummadisetti Gowthami, Bhuvanagiri Venkata Naga Sandhya, Patta Supraja, Sanjay Kumar, Matta Durga Prakash, Device-Simulation-Based Machine Learning Technique and Performance Optimization of NSFET, 2024 OITS International Conference on Information Technology (OCIT), IEEE, 2024, https://doi.org/10.1109/ocit65031.2024.00073

Publisher : IEEE

Year : 2024

Performance Improvement of Spacer-Engineered N-Type Tree Shaped NSFET Toward Advanced Technology Nodes

Cite this Research Publication : Ummadisetti Gowthami, Asisa Kumar Panigrahy, Depuru Shobha Rani, Muralidhar Nayak Bhukya, Vakkalakula Bharath Sreenivasulu, M. Durga Prakash, Performance Improvement of Spacer-Engineered N-Type Tree Shaped NSFET Toward Advanced Technology Nodes, IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), 2024, https://doi.org/10.1109/access.2024.3388504

Publisher : Institute of Electrical and Electronics Engineers (IEEE)

Research Talks
  • Research presentations at international conferences including OCIT 2024 and IEEE CONNECT 2025.
Conferences
  • APSCON 2024; 2024 OITS International Conference on Information Technology (OCIT 2024), Vijayawada; APSCON 2025; IEEE CONNECT 2025; IEEE NANO 2025 (paper accepted).
Research Interest
  • Nanoelectronics and VLSI
  • Nanosheet FETs and tree-shaped NSFETs
  • Gate-All-Around devices
  • TCAD-based semiconductor device modelling
  • Spacer engineering
  • Analog/RF analysis
  • Dielectric-modulated biosensors
  • Machine-learning-assisted device modelling
  • AI/ML applications
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