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SignOff Semiconductors

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Lead Physical Design Engineer

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Summary

Lead a physical design team taking high-performance SoCs from RTL to GDSII at SignOff Semiconductors in Bangalore: floorplanning, CTS, routing, MMMC timing closure, power intent (UPF/CPF) and EM/IR checks, and DRC/LVS sign-off, using Synopsys/Cadence flows (ICC2, Fusion Compiler, Innovus, PrimeTime) plus TCL/Python/Perl flow automation.

Role Overview

We are seeking a highly motivated Lead Physical Design Engineer to drive the physical implementation of next-generation, high-performance SoCs. The role involves leading physical design activities from RTL-to-GDSII, mentoring engineers, and ensuring successful tape-outs with optimal power, performance, and area (PPA).

Key Responsibilities

  • Lead and mentor a team of Physical Design engineers.
  • Drive end-to-end implementation including Floorplanning, Power Planning, Placement, CTS, Routing, and PPA optimization.
  • Perform timing closure across MMMC scenarios using STA methodologies.
  • Execute and analyze EM/IR, power intent (UPF/CPF), and reliability checks.
  • Achieve sign-off quality for DRC, LVS, ERC, and Antenna verification.
  • Collaborate with RTL, DFT, Verification, and Architecture teams to resolve design challenges.
  • Develop and enhance automation flows using TCL, Python, and Perl.

Requirements

Required Qualifications

  • Bachelor's or Master's degree in Electrical/Electronics Engineering or related field.
  • 6-9 years of hands-on experience in Physical Design.
  • Strong expertise in ASIC implementation and advanced node design (7nm/5nm/3nm preferred).
  • Experience with:
    • ICC2 / Fusion Compiler or Cadence Innovus
    • PrimeTime or Tempus
    • Calibre / ICV / Pegasus
    • RedHawk or Voltus
  • Proficiency in TCL, Python, Perl, and Make.
  • Strong problem-solving, debugging, and stakeholder management skills.

Preferred Qualifications

  • Experience with CPU/GPU or high-performance SoC designs.
  • Exposure to advanced packaging technologies such as 2.5D, 3D-IC, and Chiplets.
  • Familiarity with AI/ML-driven EDA optimization methodologies.



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