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48‐3: Distinguished Student Paper: AI‐Algorithm‐Driven Automated Layout Generation Method for Flat Panel Display with High Aperture‐Ratio and Charging‐Ratio.

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Τίτλος: 48‐3: Distinguished Student Paper: AI‐Algorithm‐Driven Automated Layout Generation Method for Flat Panel Display with High Aperture‐Ratio and Charging‐Ratio.
Συγγραφείς: Tang, Haodong, Zeng, Haohang, Yuan, Weiming, Qiu, Zhiren, Zou, Lanrong, Liao, Congwei, Zhang, Shengdong
Πηγή: SID Symposium Digest of Technical Papers; Aug2026 Supplement 1, Vol. 57, p478-481, 4p
Θεματικοί όροι: Flat panel displays, Deep learning, Optical apertures, Software libraries (Computer programming), Simulation Program with Integrated Circuit Emphasis
Περίληψη: This paper presents an AI‐driven layout automated generation method for flat panel displays (FPDs) layouts. By using PyAether's Python API to design code, this work automates layout generation, RC extraction, SPICE simulation, and optimizations. Pixel parameters of width and length of TFT, and the geometric shapes of gateline and dataline are automatically optimized within 36 seconds. Furthermore, deep learning algorithms, including sequential quadratic programming (SQP), simulated annealing (SA), and artificial neural networks (ANNs) optimization were used to improve the charging ratio. The proposed method obtains a 13.02% aperture ratio improvement while ensuring ≥90% charging ratio. [ABSTRACT FROM AUTHOR]
Copyright of SID Symposium Digest of Technical Papers is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 48‐3: Distinguished Student Paper: AI‐Algorithm‐Driven Automated Layout Generation Method for Flat Panel Display with High Aperture‐Ratio and Charging‐Ratio.
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  Data: <searchLink fieldCode="AR" term="%22Tang%2C+Haodong%22">Tang, Haodong</searchLink><br /><searchLink fieldCode="AR" term="%22Zeng%2C+Haohang%22">Zeng, Haohang</searchLink><br /><searchLink fieldCode="AR" term="%22Yuan%2C+Weiming%22">Yuan, Weiming</searchLink><br /><searchLink fieldCode="AR" term="%22Qiu%2C+Zhiren%22">Qiu, Zhiren</searchLink><br /><searchLink fieldCode="AR" term="%22Zou%2C+Lanrong%22">Zou, Lanrong</searchLink><br /><searchLink fieldCode="AR" term="%22Liao%2C+Congwei%22">Liao, Congwei</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Shengdong%22">Zhang, Shengdong</searchLink>
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  Data: SID Symposium Digest of Technical Papers; Aug2026 Supplement 1, Vol. 57, p478-481, 4p
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  Data: <searchLink fieldCode="DE" term="%22Flat+panel+displays%22">Flat panel displays</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+apertures%22">Optical apertures</searchLink><br /><searchLink fieldCode="DE" term="%22Software+libraries+%28Computer+programming%29%22">Software libraries (Computer programming)</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+Program+with+Integrated+Circuit+Emphasis%22">Simulation Program with Integrated Circuit Emphasis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper presents an AI‐driven layout automated generation method for flat panel displays (FPDs) layouts. By using PyAether's Python API to design code, this work automates layout generation, RC extraction, SPICE simulation, and optimizations. Pixel parameters of width and length of TFT, and the geometric shapes of gateline and dataline are automatically optimized within 36 seconds. Furthermore, deep learning algorithms, including sequential quadratic programming (SQP), simulated annealing (SA), and artificial neural networks (ANNs) optimization were used to improve the charging ratio. The proposed method obtains a 13.02% aperture ratio improvement while ensuring ≥90% charging ratio. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of SID Symposium Digest of Technical Papers is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/sdtp.70003_123
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        Text: English
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        Type: general
      – SubjectFull: Deep learning
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      – SubjectFull: Optical apertures
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              Text: Aug2026 Supplement 1
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              Y: 2026
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