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2024-06-12 08:00:58 | onclick: | Improvement of Equivalent Performance Algorithm for Wiring Structure

Recently, WGS PhD students in our research group have been working to improve the partitioned equivalent material performance algorithm for IC products, aiming to achieve 15 to 30 times computational efficiency without changing hardware and software configuration.
In the afternoon of June 6th, WGS students talked about the progress of their work and several problems they faced.WGS students suddenly sent a WeChat message at 10 p.m. last night, introduced its latest progress and showed off its development of a very friendly graphical user interface.This morning we exchanged further technical details.Excerpts from the exchange are as follows:
WGS students:
Mr. Jia good evening, I basically handled the wiring layer partition homogenization material parameters of the batch calculation tool, made a graphical interface and compiled into an executable program.The software can set the number of partition pixels, set the signal layer and power layer calculation packet, set the material parameters, and batch deployment and batch submission of calculation files.At the same time, the intelligent inspection function of the calculation process is set up to check the process of unexpected exit; and the function of merging the data is set up to merge the result data of different groups into one after the calculation is completed.
The Seal of Jia:
With this particularly efficient algorithm, the simulation accuracy of the equivalent material properties of the distribution structure is basically unchanged, right?Or, the simulation accuracy has been reduced, but the reduction is limited, below a certain value, such as 5%.
WGS students:
Mr. Jia, the results obtained from my side test are completely consistent with the results we calculated before, so you can let the students test more.
The Seal of Jia:
Is your particularly efficient algorithm not suitable for partitioned equivalent performance calculations of non-mechanical physical quantities (such as thermal conductivity)?Or is it impossible to calculate the partitioned equivalent performance acceleration for non-mechanical physical quantities (e.g., thermal conductivity)?
WGS students:
The program itself is only responsible for data deployment and batch commit calculations, and when calculating other parameters, you can change the called calculation script.The thermal conductivity itself is calculated quickly and should not be adjusted.
The Seal of Jia:
I'm thinking about this new approach to data deployment and batch submission computation, and you've created a new algorithm tool platform and developed a graphical user interface that can improve the efficiency of partition equivalence.Faced with such a significantly more efficient, automated, and intelligent self-testing tool platform, our team will certainly embrace it, greatly accelerating the construction of our partitioned equivalent performance dataset for wiring structures.This order of magnitude improvement in cost-effectiveness is a temptation that any person directly involved in a career can't resist.Next, how can our team's research ideas and priorities be adjusted accordingly?
WGS students:
Jia, this time it's equivalent to two updates.The first update is to use two steps in a job to calculate the engineering constant and the coefficient of thermal expansion; where, there are six loadcases in the first step to calculate the engineering constant separately.The second update is the addition of bulk deployment and computation, avoiding the need to commit one by one when using multiplexing, and avoiding negligence and errors, which are important for parallel computation with more cores.The two updates are relatively independent, and mass-deployed programs can still be used to call scripts that we previously developed to calculate equivalent parameters for cyclical cells, or to call any script such as embedded cells and thermal conductivity.However, the input parameters for calculating thermal conductivity need to be adjusted appropriately, which is not complicated.
A little thought:
I always think that the AI training model of high-precision near-sea data with its own labeling function is the key to building the industry's AI model.At present, we take the reliability analysis of integrated circuit products as a case, and we have indeed taken this road, believing that we can promote the landing of the AI+ industry.
We welcome your valuable suggestions to jointly promote the development of data and algorithm technology under realistic computing conditions, thereby promoting the high efficiency and low cost landing of AI+ industry.

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