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Raveesh Magod | Researchclopedia

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Raveesh Magod

Indian Institute of Technology Madras ORCID

13

Works

177

Citations

5

h-index

3

i10-index

Research Topics

Low-power high-performance VLSI design Advancements in Semiconductor Devices and Circuit Design Analog and Mixed-Signal Circuit Design

Affiliations

Indian Institute of Technology Madras2026–2026

Arizona State University2015–2018

Texas Instruments (United States)2020–2025

Top Works

A 1.24 <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math> </inline-formula>A Quiescent Current NMOS Low Dropout Regulator With Integrated Low-Power Oscillator-Driven Charge-Pump and Switched-Capacitor Pole Tracking Compensation

Raveesh Magod,Bertan Bakkaloğlu,et al.

201883 citations

Semiconductor materials and devices

Advanced DC-DC Converters

A Low-Noise Output Capacitorless Low-Dropout Regulator With a Switched-RC Bandgap Reference

Raveesh Magod,Naveen Suda,et al.

201652 citations

32.5 A Scalable and PCB-Friendly Daisy-Chain Approach to Parallelize LDO Regulators with 2.613% Current-Sharing Accuracy Using Dynamic Element Matching for Integrated Current Sensing

Bhushan Talele,Raveesh Magod,et al.

202011 citations

A 96.1% Efficiency Single-Inductor Multiple-Output (SIMO) Buck Converter With 2.1-A/ns Transient Speed and 2.2-A Maximum Current Capacity

Lei Zhao,Junyao Tang,et al.

20249 citations

A 14.8&#x03BC;V<inf>RMS</inf> integrated noise output capacitor-less low dropout regulator with a switched-RC bandgap reference

Raveesh Magod,Naveen Suda,et al.

20157 citations

A 65nm Fully-integrated Fast-switching Buck Converter with Resonant Gate Drive and Automatic Tracking

Xi Chen,Aly Shoukry,et al.

20235 citations

An Active EMI Cancellation Technique Achieving a 25-dB Reduction in Conducted EMI of LIN Drivers

Abhishek Ray,Raveesh Magod,et al.

20224 citations

Proactive Power Regulation with Real-time Prediction and Fast Response Guardband for Fine-grained Dynamic Voltage Droop Mitigation on Digital SoCs

Xi Chen,Jiaxiang Feng,et al.

20234 citations

A 65-nm Proactive Power Management Technique With Real-Time Machine Learning Engine for Droop Prediction and Mitigation on Microprocessors

Xi Chen,Jiaxiang Feng,et al.

20243 citations