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LSTM-based intelligent generation method for dispensing graph

A graphics and dispensing technology, applied in neural learning methods, biological neural network models, instruments, etc., to save material costs, reduce training parameters, and shorten the test cycle.

Pending Publication Date: 2021-03-12
BEIJING RES INST OF TELEMETRY +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The present invention aims to solve the problem that the artificial intelligence algorithm cannot be introduced into the parameter decision-making system of electronic packaging, and provides an intelligent generation method of dispensing graphics in the field of electronic packaging

Method used

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  • LSTM-based intelligent generation method for dispensing graph
  • LSTM-based intelligent generation method for dispensing graph
  • LSTM-based intelligent generation method for dispensing graph

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0039] Such as figure 1 As shown, an LSTM-based method for intelligently generating dispensing graphics includes the following steps:

[0040] S1. Data preparation: collect the input features and output features of the automatic placement data to form a data set, and divide the data set into a training set, a verification set and a test set;

[0041] S2. Establishing an input feature attribute vector: preprocessing the input feature to obtain an input feature attribute vector;

[0042] S3. Establishing an output feature attribute vector: preprocessing the output feature to obtain an output feature attribute vector;

[0043] S4, establish LSTM model: such as Figure 2-3 As shown, design the LSTM structure and hyperparameters to establish the LSTM model;

[0044] S5. Training LSTM model: Design the loss function and optimization algorithm of the LSTM model, use the input feature attribute vector and the output feature attribute vector to perform LSTM model training, correct h...

Embodiment 2

[0047] Such as figure 1 As shown, an LSTM-based method for intelligently generating dispensing graphics includes the following steps:

[0048] S1. Data preparation: collect the input features and output features of automatic placement data to form a data set, divide the data set into training set, verification set and test set; input features include: numerical features, ordinal features and nominal features; output The feature is the coordinate sequence of the dispensing pattern; the total number of samples in the data set is not less than 1000 groups, and the proportions of the training set, verification set and test set are: 70%, 20%, 10%;

[0049] S2, establish the input feature attribute vector: such as Figure 4 As shown, the input feature is preprocessed to obtain the input feature attribute vector;

[0050] S21. Define numerical features, and perform normalization processing on the numerical features to obtain numerical feature vectors; the numerical features are chip ...

Embodiment 3

[0062] Such as figure 1 As shown, for a set of existing automatic placement process parameters and corresponding dispensing patch effects (given according to the GJB548B evaluation standard), the sample size of the data set is 1000, and an LSTM-based intelligent generation method for dispensing graphics is designed , including the following steps:

[0063] S1. Data preparation: collect the input features and output features of automatic placement data to form a data set, divide the data set into training set, verification set and test set; input features include: numerical features, ordinal features and nominal features; output The feature is the coordinate sequence of the dispensing pattern; the total number of samples in the data set is not less than 1000 groups, and the proportions of the training set, verification set and test set are: 70%, 20%, 10%;

[0064] S2, establish the input feature attribute vector: such as Figure 4 As shown, the input feature is preprocessed t...

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Abstract

The invention provides an LSTM-based intelligent generation method for a dispensing graph, which comprises the steps of collecting input features and output features of automatic mounting data to forma data set, preprocessing the input features to obtain an input feature attribute vector, preprocessing the output features to obtain an output feature attribute vector, designing an LSTM structure and hyper-parameters to establish an LSTM model; designing a loss function and an optimization algorithm of the LSTM model, performing LSTM model training by using the input feature attribute vector and the output feature attribute vector respectively, correcting hyper-parameters until the training is finished to obtain a final LSTM model, and calling the final LSTM model to generate a dispensing graph. The invention provides the intelligent generation method for the dispensing graph in the field of electronic packaging, and the method combines a long-short-term memory unit LSTM in a recurrentneural network RNN in the field of artificial intelligence with a dispensing graph generation process, thereby providing a feasible scheme for the generation of parameters with a time sequence. Therefore, the problem of time information loss caused by the fact that dispensing pattern selection is carried out only through a process test and a feedforward neural network is used for generating a pattern is solved.

Description

technical field [0001] The invention relates to the technical field of semiconductor devices, in particular to an LSTM-based intelligent generation method for dispensing graphics. Background technique [0002] Automatic placement is a key process that determines the performance and accuracy of electronic packaging products, and the result of automatic placement is determined by multiple parameters of automatic dispensing and placement. The determination of the dispensing pattern affects the effect of automatic placement key step. In the traditional process, the engineer will select the dispensing pattern based on past experience, then conduct a large number of process tests, and continuously adjust the dispensing pattern according to the placement effect. The same chip often needs to be iterated multiple times, resulting in a lot of waste of time and material costs. Engineers The relationship between the inherent characteristics of the chip and the dispensing pattern has no...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06N3/04G06N3/08G06F113/18
CPCG06F30/27G06N3/049G06N3/08G06F2113/18G06N3/045Y02P90/30
Inventor 杜仲辉刘德喜井津域史磊康楠刘洋景翠
Owner BEIJING RES INST OF TELEMETRY
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