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MEMS speaker yield optimization

Optimizing MEMS microspeaker yield & audio quality via MultiphysicsAI

 

10-2
Electrostatics   •   Solid mechanics   •   Laminar flow  •   Acoustic waves   •  Fluid-Structure Interaction   •   Mesh deformation

The challenge

Silicon-based MEMS speakers face a difficult trade-off: driving them harder increases volume (SPL) but introduces Harmonic Distortion (THD) due to nonlinear electrostatic effects.

Furthermore, microscopic manufacturing variations (tolerances of ±10%) can ruin performance. Traditional simulation is too slow to map these thousands of failure scenarios.

Approach with Quanscient Allsolve

The team performed a Nonlinear Harmonic Balance analysis on the cloud, generating 12,500 simulations in just 20 minutes.

This data trained an AI Surrogate Model which performed a Monte Carlo analysis, predicting how manufacturing tolerances would affect the final production yield.

Key results

  • Performance boost: Identified a Pareto-optimal design with ~30% higher SPL while keeping THD below 0.15% (far exceeding the 1% target).
  • Yield prediction: The AI predicted a 72% manufacturing yield given ±10% geometric variations, allowing engineers to adjust tolerances before cutting silicon.
  • Speed: The AI Surrogate provides instant uncertainty quantification, replacing weeks of prototype testing.
Yield analysis showing designs that pass or fail the set criteria. Green dots indicating designs that pass both criteria of SPL and THD of pressure.