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Generation of Two port PiezoMEMS S-parameters using LynxAI
deep learning AI powered platform

There is a lot of headway being made in machine learning, deep learning, and AI today.

There is a growing need to apply AI to manufacturing processes to automate data collection and ensure consistent product quality. However, AI can also be applied to provide advanced design collateral or development kits that accelerate the customer’s product development.

Integrated PiezoMEMS on a CMOS silicon substrate is a turn-key, application dependent business. PiezoMEMS are transducers that
convert mechanical displacements to electrical impulses and vice-versa. There is a vast range of PiezoMEMS applications from signal processing to physical sensing domains. The system level design requires, at a minimum, the frequency response from the PiezoMEMS device.

The frequency response is typically extracted using a network analyzer and measuring the transfer function or scattering parameter response. With this set up, the PiezoMEMS is actuated by an AC signal with varying frequency, and the PiezoMEMS output is sensed and plotted with respect to the input signal. 

This whitepaper describes a methodology to employ measured PiezoMEMS frequency response data to generate a model from LynxAI platform. The  articular case study corresponds to a PiezoMEMS device made of a free-standing membrane with different diameter sizes which will determine the resonance frequency. The measured data employed in the study has been provided by SilTerra Malaysia Sdn. Bhd.