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Model the array, not one cell

PMUT and CMUT simulation with electrical, mechanical, and acoustic physics coupled in one model.

Array-scale  •  Coupled multiphysics  •  Python SDK

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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

Single-cell simulation hides the array

The effects that decide whether a transducer works — crosstalk, radiation, real beam shape — are array-level. Model one cell at a time and you don't see them until the wafer comes back.

01

The array won't fit in memory

So you simulate element by element and stitch assumptions together. The array-level behaviour is never solved.

02

Crosstalk is left out

Inter-element coupling doesn't enter a single-cell model, then reappears as artefacts in the measured beam profile.

03

Model and measurement diverge

Simplified models can lead to serious discrepancies with experiment, so the prototype is the first honest look at the device.

04

CMUT setup is a bottleneck

Full CMUT models are hard enough to set up that one specialist becomes the constraint on the whole project.

What Allsolve does

Array-scale MUT simulation, fully coupled

One model, all the physics, at array scale, run on the cloud and driven from your own scripts.

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Array-scale modelling

Crosstalk, acoustic radiation, and propagation solved together across the array, including viscoelastic damping in packaging and lens layers that acoustic-only tools skip.

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Natively coupled physics

Electrical, mechanical, and acoustic interactions in one unified model, solved simultaneously rather than handed sequentially between separate solvers.

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Cloud scale, from code

Sweep hundreds of design variants overnight with parallel cloud compute. Write the loop once in the Python SDK and feed results straight into surrogate training.

Proven on real MUT designs

What that makes possible

Two PMUT examples of the kind of study that isn't practical when every run takes hours on a desktop.

MultiphysicsAI — Generative design

65 → 100%

Fractional bandwidth, from parametric limit to inverse-designed

10,000 simulations trained an AI surrogate that predicts device physics in under a millisecond. Inverse design then pushed PMUT fractional bandwidth from 65% to roughly 100% while holding the 12 MHz centre frequency.

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Monte Carlo — Yield

260× faster

1,000 runs in 15 minutes, not 66 hours

A yield study ran 1,000 Monte Carlo simulations in about 15 minutes instead of 66 hours, predicting 71.9% manufacturing yield for a strongly coupled piezoelectric-acoustic sensor and flagging oxide thickness as the critical parameter.

Open the full example →

Real-world examples

The business case

Every avoided prototype run pays for the tool

MEMS development is expensive because each device needs its own bespoke process, built and validated across multiple fab-dependent stages. A medium-complexity device runs to a minimum of ~$4M in engineering budget over roughly four years. Every prototype iteration you can resolve 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.
FAQ

PMUT and CMUT simulation questions

Can Allsolve simulate a full PMUT or CMUT array instead of a single cell?
Yes — at array scale. It models a representative multi-element section rather than a single cell, so crosstalk, acoustic radiation, and beam formation are captured directly. For arrays of thousands of cells that means a representative section, not every individual cell, which is what array-level behaviour requires.
Does it capture crosstalk between array elements?
Yes. Because array elements are solved together with coupled physics, inter-element crosstalk and acoustic coupling appear in the result, rather than surfacing as artefacts only when the prototype is measured.
How does it 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 tend to ignore.
How much faster is cloud simulation than desktop FEM for MUTs?
In a published PMUT yield study, 1,000 Monte Carlo runs completed in about 15 minutes on the cloud versus roughly 66 hours on a desktop, about 260 times faster, which makes 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% yield and identified oxide thickness as the critical parameter driving it.
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 it 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 an array-scale CMUT simulation running in practice.
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Resources

Case examples and other resources

Webinar + PDF summary

Accelerating MUT design with cloud-based multiphysics simulation

Download the PDF summary covering case examples of design optimization of CMUT and PMUT devices with Quanscient Allsolve.

Download PDF summary →

Case example

Enhancing MUT design and performance with Quanscient Allsolve

Designing high-performance MUT arrays is challenging due to limited simulation capabilities. See how Allsolve has changed this.

Read the full article →

Case example

Optimizing designs by simulating thousands of design variations

Explore a case study covernig how to run thousands of simulations in parallel with Quanscient Allsolve.

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Quanscient Allsolve

See how it could work for you

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“Quanscient Allsolve's unique capabilities have helped us push the boundaries of what can be simulated in ultrasonic MEMS design.”

Nilavazhagan Subbiah, PhD — Staff Engineer, Infineon

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