{"id":5987,"date":"2025-07-11T15:11:46","date_gmt":"2025-07-11T06:11:46","guid":{"rendered":"https:\/\/aizoth.com\/?post_type=use_case&p=5987"},"modified":"2026-04-21T09:27:28","modified_gmt":"2026-04-21T00:27:28","slug":"contents-e008","status":"publish","type":"use_case","link":"https:\/\/aizoth.com\/en\/use_case\/contents-e008\/","title":{"rendered":"Multi-Objective Optimization of MEMS Sensor Manufacturing Process with Multi-Sigma\u00ae"},"content":{"rendered":"\n

We used the AI analysis platform, Multi-Sigma\u00ae<\/sup>, <\/strong>
to predict and analyze factors and optimize the manufacturing of high-performance MEMS sensors<\/strong><\/h2>\n\n\n\n

<\/p>\n\n\n\n

1. Prediction of performance indicators <\/strong>
(sensitivity, linearity, signal-to-noise ratio)<\/strong><\/h2>\n\n\n\n

Multi-Sigma\u00ae<\/sup><\/strong>‘s AI prediction function enables the training of an AI model using input data (explanatory variables) and output data (objective variables) to establish the relationship between them. This AI model can predict three performance indicators\u2014sensitivity, linearity, and signal-to-noise (S\/N) ratio\u2014based on seven manufacturing parameters: etching time, etching temperature, deposition pressure, deposition temperature, deposition time, exposure amount, and development time.<\/p>\n\n\n\n

\"1.<\/figure>\n\n\n\n
<\/div>\n\n\n\n

2. Contribution Analysis of the Manufacturing Process<\/strong><\/h2>\n\n\n\n

Multi-Sigma\u00ae<\/sup><\/strong>‘s contribution analysis allows you to identify manufacturing process conditions that positively or negatively impact the performance indicator.<\/p>\n\n\n\n

\n
\n

Sensitivity:
Deposition time (23.7%),
Deposition temperature (18.0%), \u00a0\u00a0\u00a0
Etching time (16.5%)

Linearity:
Etching Temperature (29.8%),
Etching Time (29.1%), \u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0
Exposure amount (12.9%)

S\/N ratio:
Development time (39.3%),
Exposure amount (37.4%)<\/p>\n<\/div>\n\n\n\n

\n
\"2.<\/figure>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n

3. Optimization to maximize sensor performance<\/strong><\/h2>\n\n\n\n

Multi-Sigma\u00ae<\/sup><\/strong>‘s optimization suggests the optimal combination of manufacturing parameters to achieve the desired performance indicators.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Expected outcomes:
<\/strong>1. Improved efficiency of the manufacturing process
2. Reduced experimental cost
3. Shortened product development time<\/p>\n\n\n\n

Advantages of Optimization with Multi-Sigma\u00ae<\/sup> :<\/strong>
Multi-objective optimization is achievable. By considering the interactions between objective variables, optimal manufacturing process conditions can be determined, even when conflicting effects exist. Additionally, the optimization can be customized to the defined scope of the manufacturing process.<\/p>\n\n\n\n

<\/p>\n\n\n

\n
\"3.<\/figure>\n<\/div>\n\n\n

<\/p>\n\n\n\n

Note: The data used in this analysis was synthesized to mimic real-world data.<\/p>\n\n\n\n

<\/div>\n\n\n\n
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Contact Us<\/strong><\/a><\/div>\n<\/div>\n\n\n\n

<\/p>\n\n\n\n

\n
Free Trial<\/strong><\/a><\/div>\n<\/div>\n\n\n\n

<\/p>\n\n\n\n

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Joint Research<\/strong><\/a><\/div>\n<\/div>\n\n\n\n

<\/p>\n\n\n\n

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Consulting<\/strong><\/a><\/div>\n<\/div>\n\n\n\n

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