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Real-Time Cell Gap Estimation in LC-Filled Devices Using Lightweight Neural Networks for Edge Deployment.

Chi-Yen Huang¹, You-Lun Zhang², Su-Yu Liao²

¹Graduate Institute of Photonics, National Changhua University of Education, Changhua 50007, Taiwan.

Nanomaterials (Basel, Switzerland)

|August 27, 2025

Summary

A new machine learning model accurately measures liquid crystal (LC) cell gaps from transmission spectra. This lightweight framework enables portable, real-time quality control for optical devices.

Keywords:

cell gap edge computing liquid crystal (LC)machine learning multilayer perceptron (MLP)transmission spectroscopy

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Area of Science:

Optoelectronics
Materials Science
Machine Learning

Background:

Accurate liquid crystal (LC) cell gap measurement is crucial for optical device performance.
Birefringent materials in LC cells distort transmission spectra, hindering traditional analysis.

Purpose of the Study:

To develop a lightweight machine learning framework for estimating LC cell gap from transmission spectra.
To enable accurate, non-destructive cell gap determination in filled LC cells.

Main Methods:

A shallow multilayer perceptron (MLP) model was trained on experimentally acquired transmission spectra.
Peak-to-peak interferometry provided ground truth cell gap values for training.
Optimization algorithms, activation functions, and neuron configurations were systematically evaluated.

Main Results:

The optimal MLP model achieved a correlation coefficient near 1 and root-mean-square error (RMSE) below 0.1 μm.
The model demonstrated real-time inference on a Raspberry Pi 4 with low latency and resource consumption.
Successful deployment validated the model for portable, edge-based applications.

Conclusions:

A lightweight MLP framework provides accurate and efficient LC cell gap estimation from transmission spectra.
The developed system is suitable for in situ diagnostics and quality control in LC-based optical applications.
Edge-based deployment enables portable and real-time inspection capabilities.