Nitrogen-Vacancy Centers in Diamond for Current Imaging at the Redistributive Layer Level of Integrated Circuits
Antoine Nowodzinski (1), Mayeul Chipaux (2), Lo\"ic Toraille (2),, Vincent Jacques (3), Jean-Fran\c{c}ois Roch (3), Thierry Debuisschert (2), ((1) CEA-LETI Grenoble France, (2) Thales Research & Technology Palaiseau, France, (3) Laboratoire Aim\'e Cotton Orsay France)

TL;DR
This paper introduces a new magnetic current imaging technique using nitrogen-vacancy centers in diamond, enabling fast, high-resolution, room-temperature measurements of current paths in integrated circuits with promising accuracy and practicality.
Contribution
The paper presents a novel NV center-based method for magnetic current imaging in ICs, offering faster, more robust, and room-temperature operation compared to existing technologies.
Findings
High-resolution magnetic field measurements over 50x200 μm^2 area.
Fast acquisition time of around 10 seconds.
Good agreement between measured and theoretical current paths.
Abstract
We present a novel technique based on an ensemble of Nitrogen-Vacancy (NV) centers in diamond to perform Magnetic Current Imaging (MCI) on an Integrated Circuit (IC). NV centers in diamond allow measuring the three components of the magnetic fields generated by a mA range current in an IC structure over a field of 50 x 200 {\mu}m^2 with sub-micron resolution. Vector measurements allow using a more robust algorithm than those used for MCI using Giant Magneto Resistance (GMR) or Superconducting Quantum Interference Device (SQUID) sensors and it is opening new current reconstruction prospects. Calculated MCI from these measurements shows a very good agreement with theoretical current path. Acquisition time is around 10 sec, which is much faster than scanning measurements using SQUID or GMR. The experimental set-up relies on a standard optical microscope, and the measurements can be…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
