AI Infrastructure

NVIDIA

Hardware debug platforms for next-generation AI/HPC systems.

DFX, scan/JTAG state capture, pre-silicon verification, and debug tooling, focused on making complex silicon observable and repeatable to debug.

Selected outcomes

Measured impact from silicon DFT work.

Quantified results from DFT methodology and test-economics work on high-volume silicon (Intel, 2021-2024).

Multi-$M
Annual test-cost savings
~20%
Yield increase
~15%
Spec-validation efficiency
~10%
Scan / test-time efficiency

Current role

Senior DFX Methodology Engineer

  • DFX methodology and DFT for AI/HPC silicon platforms (IEEE 1149.1 / 1500 / 1687, JTAG / IJTAG).
  • State capture and observability: scan dump, RAM dump, IO BIST, memory BIST, and DFD flows.
  • Internal scan-debug GUI tooling used across multiple engineering teams.
  • Pre-silicon verification and post-silicon bring-up / debug.
  • DFX security and test-access discipline for complex parts.
  • Cross-team enablement turning low-level silicon behavior into repeatable workflows.

Instruments

What “observable silicon” actually means.

The access mechanisms that turn otherwise-invisible internal behaviour into something you can read out and debug.

TDITDO

Scan / JTAG

Shift internal state out a serial path (IEEE 1149.1).

Die & package

Reach state the package hides: lid, die, interposer.

BIST

Memory / IO BIST

Built-in self-test drives arrays and reads back results.

Capture & trace

Deterministic capture, replay, and root-cause workflows.

Earlier work

Systems thinking before silicon observability.

2021 - 2024
Intel

Product Development Engineer: test methodology, RTL, and test programs (C#/Perl) for repair, raster, and scan features; ATPG/coverage analysis and large-scale JMP data analysis. Improved scan-testing efficiency and reduced test time ~10%, boosted spec-validation efficiency ~15% and testing accuracy ~20%, and drove a ~20% yield increase with multi-million-dollar annual test-cost savings.

2019 - 2021
Earlier research

Materials-science and additive-manufacturing research at Zeda (formerly PrinterPrezz), Northeastern University, and DMRL: a systems view of reliability, from physical defects to test economics.

Education
Northeastern · IIT Kanpur

M.S. Mechanical Engineering (materials science), Northeastern University; B.Tech Materials Science & Engineering, IIT Kanpur.

Next step

For roles or conversations around AI hardware observability.