COMPUTATIONAL
LITHOGRAPHY &
EDGE ML

Electronics & Communication Engineering student. I build data-driven optimization tools for semiconductor manufacturing, focusing on sub-wavelength physics and embedded intelligence.

01. Technical Skills

Process & Yield

  • SPC / DOE
  • Computational Lithography
  • EUV / DUV Modeling
  • EWMA / CUSUM

Software & AI

  • Python / C++
  • XGBoost / Ensemble ML
  • FastAPI / Docker
  • Time-Series Analytics

Hardware

  • Verilog / RTL
  • TinyML / Edge Devices
  • SkyWater 130nm PDK
  • Altium PCB Design

02. Projects

AttoFab

Engineered an open-source, Python-based computational simulation engine targeting sub-wavelength fabrication and pattern transfer profile validation under EUV and DUV conditions.

$R = k_1 \cdot \frac{\lambda}{NA}$
Python Numerical Modeling

SPC Yield Analytics

Automated excursion monitoring for manufacturing wafers, modeling a 20-40% reduction in defective starts. Achieved 90% anomaly detection using EWMA/CUSUM charts.

Time-Series SPC / EWMA

AttoSense

Accomplished production-ready NLU for intent analysis using zero-shot discovery and multimodal auditing. Architected a FastAPI microservice utilizing Groq Whisper and Llama-3.2-Vision.

Multimodal AI FastAPI

SECOM Sensor ML

Achieved 90% accuracy in semiconductor wafer defect classification to prevent process drift. Optimized 8 ensemble models on imbalanced sensor arrays, yielding 95% ROC-AUC.

Ensemble ML XGBoost

CAPHA TinyML

Autonomous, real-time inference framework for low-power microcontrollers featuring proactive anomaly detection. Integrated local models without cloud reliance.

TinyML Embedded C

Double_PINN

A differentiable pipeline bridging process physics and device physics.

AttoCore

The single-neuron Leaky Integrate-and-Fire (LIF) core Simulator.

Physics-Informed NN SNN

03. Experience

AI & ML Intern

Infosys Springboard

Feb 2026 - Apr 2026

Served as a Project Associate in the NLU Bot Trainer program, training and optimizing data pipelines for industrial intent classification. Analyzed natural language dataset patterns to refine intent detection accuracy.

Embedded & IoT Intern

Uni Convergence Tech

May 2025 - Jun 2025

Prototyped local TinyML inference engines for the CAPHA IoT system, enabling low-latency anomaly detection directly on edge microcontrollers within strict RAM/Flash memory constraints.

Product Intern

Sri Nikhil Krishna Solutions

Nov 2023 - May 2024

Conducted high-precision electrical characterization and functional quality control procedures for optoelectronic PCB assemblies. Optimized testing protocols to evaluate hardware quality.

04. Research & Education

Early Warning System for At-Risk Students (SHAP & OULAD)

EdArXiv | 2026

DOI: 10.35542/osf.io/ga46q_v1

Analytical Redundancy for False Alarm Suppression in Edge SPC

SSRN | July 2026

DOI: 10.2139/ssrn.7211483

Risk factors and their Shap values Dataset

IEEE DataPort | Jan 2025

DOI: 10.21227/SVQF-PN8

Academic Background

  • BTech in ECE

    Adikavi Nannaya University (AKNU)

    Exp. April 2027 (3rd Year)

  • Semiconductor Fabrication & Lithography

    CeNSE, IISc Bengaluru (Summer School)

    2026

  • Semiconductor Fabrication 101

    Purdue Univ, UT Austin, Intel Corp

    May 2025

  • Undergrad Diploma in ECE

    SBTET A.P., Gov. Polytechnic

    May 2024

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