Santosh Raghavan Email & Phone Number

Santa Clara, CA, US

Mountain View, California, US

Developing the world's fastest AI inference solution in the market!Responsible for:- Power Optimization & Power Modeling of Silicon & Systems for AI/ML Workloads (like LLMs)- ASIC Low-Power Design Optimization & Technology-specific Design Methodology- Power v/s Perf Architecture Trade-off for Silicon & Systems Design- Power Delivery Network design & Power.

Santa Clara, California, US

- Technology Benchmarking and Big data analysis, Signal processing, Process improvement, and Technology pathfinding within Non-volatile Memory Development performance-cost constraints- Scripting and automating design flows (using Python and C++) to accelerate process and product development- Hardware Design Enablement and PDK/SPICE model development.

Carlsbad, California, US

• Advanced RF semiconductor device design, characterization, modeling for advanced CMOS, BiCMOS, HBTs, III-V and GaN HEMT devices for analog/mixed-signal/power applications
• Development and design enablement of advanced 5nm CMOS technology, comparison with previous technologies and providing improved PDK and design environment
• Developed full spectrum of BSIM/BSIMCMG based compact SPICE models for 14nm/16nm technology PDK currently used for designing PAM4 DSP high-speed optical interconnect and 5G wireless access transceiver products
• Characterized advanced node CMOS FinFET devices covering I-V, C-V, high frequency S-parameters, flicker/thermal noise, and linearity/Intermodulation-distortion (IP3) measurements
• Developed SPICE models for precision high-R resistors, ESD diodes, BJTs and capacitors
• Designed custom test-chip for 14nm FinFET device technology for thermal and flicker noise measurements, parasitic capacitance measurement of precision high-R resistors, capacitors and diodes

Santa Barbara, CA, US

• PhD graduate student at UCSB, Materials Engineering.Advisor: Prof. Susanne StemmerDomain: Growth and Characterization of Oxide Semiconductor Thin Films For Advanced ElectronicsAuthored 17 journal publications (5 first.
• Lead and managed research projects, fabricated micro- and nano-scale oxide semiconductor devices in cleanrooms
• Characterized materials quality using numerous materials analysis tools, designed materials for device applications
• Maintained operation and troubleshooting of high-end mechanical and electronic equipment for group laboratoriesSelected Projects:1. “Molecular Beam Epitaxy of High Mobility Perovskite Oxides”
• Developed a novel growth technique for high quality BaSnO3 semiconductor thin films. Demonstrated highest reported room temperature mobilities that are of interest for higher efficiency RF and power devices2. “Probing.
• Established fundamental understanding of interplay between ferroelectricity and metallicity at reduced dimensions, with implications for all ferroelectric materials

Lausanne, Vaud, CH

• Masters graduate student at EPFL, Materials Engineering.Advisor: Prof. Nava SetterDomain: Ferroelectric Materials for Advanced ApplicationsAuthored 2 first author journal publicationsPresented at 1 conferences and gave.
• Investigated the effect of ferroelectricity on graphene and fabricated high-speed non-volatile FET-based memories with long-term retention properties2. “A DFT Study of SrTiO3 – Water Interfaces”
• SrTiO3 – Water interface was modelled to find wet electron states facilitating electron transfer at the interface
• These results explain the phenomenon which will help in fabricating efficient SrTiO3 photo-catalytic electrodes for water splitting. 3. “Fabrication and Evaluation of Organic Field-Effect Transistors (OFET)”
• Fabricated OFETs based on two materials, Pentacene and P3HT (semiconducting polymer) at EPFL and CSEM (Swiss National Research Institute, Basel)
• Achieved results helped in obtaining high-performance and low-cost OFETs, suitable for applications such as TFT (thin film displays), electronic readers and RFID chips

Navi Mumbai, Maharashtra, IN

• "Reliability Analysis of Steam Reformer Tubes in Hydrogen Plant"
• Developed a new Remnant Lifetime Assessment method for RIL plant’s reformer tubes, improving assessment of creep degradation and allowing better scheduling of plant shut-downs and reduced equipment down-time

Doctor Of Philosophy (Phd), Materials Engineering

Course, Machine Learning, Deep Learning

Master Of Science, Materials Science

Bachelor Of Technology (B.Tech.), Materials Science