Can Allsolve simulate a full PMUT or CMUT array instead of a single cell?

Yes. Allsolve models the complete transducer array rather than one representative cell, so array-level effects like crosstalk, acoustic radiation, and beam formation are captured directly in one simulation instead of being approximated or left out.

Does it capture crosstalk between array elements?

Yes. Because the whole array is solved together with coupled physics, inter-element crosstalk and acoustic coupling appear in the result, rather than surfacing only as artefacts when the prototype is measured.

How does Allsolve couple electrical, mechanical, and acoustic physics?

All physics are solved simultaneously in a single model rather than passed sequentially between separate tools, including viscoelastic damping in packaging and lens layers that acoustic-only solvers often ignore.

How much faster is cloud simulation than desktop FEM for MUTs?

In a published PMUT yield study, 1,000 Monte Carlo simulations ran in about 15 minutes on the cloud versus roughly 66 hours on a desktop workflow, about 260 times faster, which makes full design-space and manufacturing-variation studies practical overnight.

Can I run manufacturing-yield or Monte Carlo studies?

Yes. Monte Carlo analysis over manufacturing variation is built in. A PMUT example predicted 71.9% manufacturing yield and identified oxide thickness as the critical parameter.

Does it fit a scripted or Python workflow?

Yes. The Python SDK lets you write a parameter sweep once and launch hundreds of variants from code, then pull the results straight into AI-surrogate training or an existing engineering pipeline.

Can Allsolve help with CMUT setup complexity?

CMUT models are notoriously hard to set up. Allsolve pairs the software with hands-on application support so the coupled model is built correctly, which is how teams get a full-array CMUT simulation running in practice.

MEMS Gyroscope Simulation | Quanscient Allsolve

Nilavazhagan Subbiah, PhDStaff Engineer

“Quanscient Allsolve enables large-scale multiphysics simulations of PMUT and CMUT arrays that would be challenging to achieve with other simulation tools. Its unique capabilities have helped us push the boundaries of what can be simulated in ultrasonic MEMS design.”

PixierayAntony Hartley, CAE Consultant

"With Quanscient Allsolve, we were able to test different parameters to find the working design from the first iteration, saving three months in product development time."

kiutraKlaus Eibensteiner, Team Lead of Engineering

“Quanscient Allsolve made our hardware iterations much more reliable and functional, speeding up the development process by requiring fewer hardware iterations to get to a finalized product.”

The gap

Q-factor is where simulation and measurement part ways

A gyroscope's performance is set by how it loses energy, and those loss mechanisms are coupled. Model them separately, or on a tool that can't scale to the next-generation design, and the Q-factor you predict won't be the one you measure.

ROM tools hit a ceiling

A reduced-order model handles today's device but can't capture the mode coupling of the next-generation design. The limit only shows up once you're designing it.

Air gaps mean re-meshing

Adding or changing an air gap in a general FEM tool can force a full re-mesh, costing days of work every time the geometry moves.

Damping solved in pieces

Anchor loss, squeeze-film, and thermoelastic damping run separately, so their combined effect on Q-factor is never captured.

The fab decision rests on a guess

With a Q-factor you don't fully trust, the commitment to fabrication is made on a model that doesn't match the bench.

What Allsolve does

Every loss mechanism in one coupled model

Mechanical, thermal, and fluid physics coupled in a single model, run across geometry and pressure on the cloud and driven from your own scripts.

All damping, solved together

Anchor loss, squeeze-film, and thermoelastic damping in one coupled model. The Q-factor you predict reflects how the device loses energy.

Q-factor and modes, directly

Dedicated eigenfrequency analysis extracts natural frequencies, mode shapes, and quality factors straight from the model, with no frequency sweep needed.

Sweeps at cloud scale

Explore beam width, proof-mass geometry, and ambient pressure across many variants efficiently in the cloud, scripted once in the Python SDK.

Proven on real inertial sensors

What that makes possible

A coupled damping study on a real gyroscope, at a scale impractical for a single workstation.

Coupled damping analysis

50%

Q-factor error corrected

A fully coupled mechanical–thermal–fluid model corrected a 50% Q-factor error by capturing squeeze-film damping across temperature, the loss mechanism single-physics models miss.

Open the full example →

Cloud solve

10 min

For a 2.4M-DOF coupled solve

That full mechanical–thermal–fluid solve, at 2.4 million degrees of freedom, ran in 10 minutes on the cloud, so anchor loss, squeeze-film, and thermoelastic damping are explored together across sweeps, not one static design at a time.

Open the full example →

The business case

Every avoided prototype run pays for the tool

Automotive and consumer inertial sensors ship at huge volume, but each new device still runs the full MEMS development path first. A medium-complexity device runs to a minimum of ~$4M in engineering budget over roughly four years. Every geometry and damping question you settle in simulation instead of silicon takes cost and months out of that path.

Source: Fitzgerald, White & Chung, "MEMS Product Development: From Concept to Commercialization" (Springer), Ch. 3, Table 3.2.

$1.5M

Minimum engineering budget for the advanced-prototype stage alone, for a medium-complexity MEMS device.

~4 years

Typical span from proof-of-concept to foundry pilot production — each fab-dependent stage is roughly a year.

$4M

Minimum total engineering budget across the four development stages, before overhead or capital expenses.

Resources

Case examples and other resources

Webinar + PDF summary

Fully coupled MEMS simulation at scale: 3D FBAR & gyros

Download the summary on running fully coupled MEMS simulations and 3D parametric sweeps on gyroscopes with Quanscient Allsolve.

Download PDF summary →

Technical example

Predicting real-world gyroscope performance via coupled damping

A coupled damping analysis that corrected a 50% Q-factor error by capturing squeeze-film damping across temperature.

Open the example →

Case example

Fully coupled MEMS gyroscope simulations with Allsolve

How anchor loss, squeeze-film, and thermoelastic damping combine in one model to predict a gyroscope's real Q-factor.

Read the full article →

FAQ

Inertial MEMS simulation questions

Can Allsolve simulate all the damping mechanisms in a gyroscope at once?

Yes. Anchor loss, squeeze-film, and thermoelastic damping are solved together in one coupled model, so their combined effect on the quality factor is captured rather than approximated mechanism by mechanism.

Why doesn't my Q-factor simulation match measurement?

Q-factor is set by coupled loss mechanisms. When they're modelled separately, or a dominant one like squeeze-film damping is left out, the predicted Q-factor diverges from the measured one. A published study corrected a 50% Q-factor error by capturing squeeze-film damping across temperature.

Does it capture squeeze-film damping in the air gap?

Yes. The thin air film between the proof mass and substrate is modelled directly as part of the coupled solve, across pressure and temperature, which is where much of a gyroscope's energy loss happens.

Can I run geometry and pressure sweeps efficiently?

Yes. Parameters such as beam width, proof-mass geometry, and ambient pressure can be swept across many variants in the cloud, so you explore the design space instead of simulating one static design at a time.

Can it extract eigenfrequencies and Q-factor directly?

Yes. Dedicated eigenfrequency analysis returns natural frequencies, mode shapes, and quality factors straight from the model, without a time-consuming frequency sweep.

Does it handle accelerometers as well as gyroscopes?

Yes. The same coupled-physics approach applies to inertial MEMS generally. The loss mechanisms and mode behaviour that govern gyroscopes govern accelerometers too.

Does it fit a Python or scripted workflow?

Yes. The Python SDK lets you script parametric sweeps and run them on the cloud, then pull results straight into an AI-surrogate or an existing engineering pipeline.

Quanscient Allsolve

See how it could work for you

Submit the form to talk with our experts—we'll respond within 1 business day. You'll learn:

How Allsolve could fit your use case
What results to expect (accuracy, runtime, design exploration capabilities and rough cost range)
How it could plug into your workflow today

Interested in just seeing an on-demand demo? Watch the 3-minute demo here

Predict Q-factor before you fabricate

Simulate mode coupling, air-gap physics, and every damping mechanism together, so the Q-factor and frequency you model are the ones you measure before the fabrication decision.