R&D Simulation for Electronics

• Slow Iterations — Each new concept required multiple physical builds and lab tests, stretching the typical prototype cycle to 6-8 weeks.
• High R&D Spend — Expensive test rigs and materials were consumed before design flaws surfaced.
• Fragmented Toolchain — Mechanical, electrical and firmware teams used separate simulation packages with no shared data backbone.
• Market Pressure — Competitors were narrowing the innovation gap, demanding faster time-to-market.

I (Danniel Nguyen) and my team spearheaded the creation of an end-to-end R&D simulation platform that unified modelling, validation and cross-team collaboration:

• Integrated Multi-Physics Engine

  • What We Did: Joined mechanical, thermal, and electromagnetic solvers under one UI.
  • Why It Mattered: Caught cross-domain design conflicts early.

• Parametric “Digital Sandbox”

  • What We Did: Allowed engineers to tweak hundreds of variables and instantly see performance deltas.
  • Why It Mattered: Replaced weeks of physical tweaks with hours of virtual experimentation.

• Automated Test Suites

  • What We Did: Regression scripts ran overnight against every new design revision.
  • Why It Mattered: Ensured every prototype met spec before hardware spend.

• Real-Time Collaboration Hub

  • What We Did: Cloud back-end synchronized CAD, BOM, and simulation data across global teams.
  • Why It Mattered: Eliminated version-control chaos and re-work.

• 20 % reduction in overall time-to-market for flagship devices.
• $3.2 M annual R&D savings in prototype materials and lab hours.
• 4× faster design iterations (from ~6 weeks to 10–12 days per cycle).
• 30 % drop in late-stage engineering change orders (ECOs).
• 24/7 workflow enabled across Asia, Europe, and North America thanks to the cloud hub.

• Virtual first, physical last — shifting failures to the digital realm slashes cost and schedule risk.
• Unified data beats best-of-breed silos — integration speed trumps isolated tool power.
• Automation compounds returns — every nightly simulation is free learning velocity.

• Multi-physics simulation suite (ANSYS + custom solvers)
• Cloud-native data lake (AWS) with GPU-accelerated compute clusters
• Python-based regression framework and CI/CD pipeline for simulation jobs
• WebGL dashboard for live results visualization