View source for 18059138. MACHINE LEARNING TOOL FOR LAYOUT DESIGN OF PRINTED CIRCUIT BOARD simplified abstract (Honeywell International Inc.)

=MACHINE LEARNING TOOL FOR LAYOUT DESIGN OF PRINTED CIRCUIT BOARD=


==Organization Name==

[[:Category:Honeywell International Inc.|Honeywell International Inc.]]


[[Category:Honeywell International Inc.]]

==Inventor(s)==

[[:Category:Apurv Jaiswal of Bengaluru (IN)|Apurv Jaiswal of Bengaluru (IN)]][[Category:Apurv Jaiswal of Bengaluru (IN)]]

[[:Category:Pradeep Apparambath of Bangalore (IN)|Pradeep Apparambath of Bangalore (IN)]][[Category:Pradeep Apparambath of Bangalore (IN)]]

[[:Category:Shana Afreen of Bengaluru (IN)|Shana Afreen of Bengaluru (IN)]][[Category:Shana Afreen of Bengaluru (IN)]]

==MACHINE LEARNING TOOL FOR LAYOUT DESIGN OF PRINTED CIRCUIT BOARD - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18059138 titled 'MACHINE LEARNING TOOL FOR LAYOUT DESIGN OF PRINTED CIRCUIT BOARD

==Simplified Explanation==

The disclosure is about a system and method for applying machine learning to estimate parasitic values of a PCB layout.

* The machine learning model is trained with various PCB layouts to accurately determine estimated parasitic impedance.
* Regions of the board with high parasitic impedance values are identified, alerting the user to reconfigure the region to achieve desired values.

== Potential Applications ==
This technology could be applied in industries such as electronics manufacturing, PCB design, and quality control to improve the performance of electronic devices.

== Problems Solved ==
This technology helps in identifying regions of a PCB layout with high parasitic impedance values, allowing for targeted reconfiguration to achieve desired impedance levels.

== Benefits ==
The system and method provide a more accurate estimation of parasitic values on a PCB layout, leading to improved performance and reliability of electronic devices.

== Potential Commercial Applications ==
"Improving Electronic Device Performance Through PCB Layout Analysis and Optimization"

== Possible Prior Art ==
There may be prior art related to machine learning applications in PCB design and analysis, as well as methods for estimating parasitic values in electronic circuits.

=== What are the limitations of this technology in real-world applications? ===

The limitations of this technology in real-world applications may include the need for a large dataset of PCB layouts for training the machine learning model, as well as the potential challenges in accurately reconfiguring regions of the board based on the estimated parasitic values.

=== How does this technology compare to traditional methods of estimating parasitic values in PCB layouts? ===

This technology offers a more automated and data-driven approach to estimating parasitic values in PCB layouts compared to traditional methods, which may rely more on manual analysis and simulation techniques.


==Original Abstract Submitted==

This disclosure is directed to a system and method for applying machine learning model to a PCB layout to obtain an estimate of the parasitic values of the board. The machine learning model may be trained with various PCB layouts to accurately determine an estimated parasitic impedance. Determine regions of the board having a high parasitic impedance value, alerting the user to reconfiguring the region so to achieve a desired range of values.



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