High performance alloy bump material and ic packaging process

Abstract

The present application relates to high-performance alloy bump material and IC packaging process, including the following technical solutions: metallographic microscope is used to detect the internal grain structure of the alloy bump, and the alloy bump is divided into at least eight heating areas; according to the detection and area division result, the working parameters of the electromagnetic induction heating device are dynamically adjusted to realize accurate control of the alloy bump heating process; the grain structure and resistivity after heating treatment are detected by the metallographic microscope and the four-probe tester to ensure product quality. The process is characterized by fine division of the heating area, dynamic adjustment of the parameters, uniform heating, improved heating efficiency, energy saving and emission reduction; at the same time, the product reliability is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to high-performance alloy bump material and IC packaging process. BACKGROUND

[0002] In the traditional IC packaging process, the alloy bump is usually processed by uniform heating method; however, this method has certain deficiencies, such as uneven heating, difficulty in controlling the grain structure, etc., and the uneven internal grain structure of the alloy bump will seriously affect the performance of the bump after packaging.

[0003] Therefore, a high-performance alloy bump material and IC packaging process are needed to solve the problems of uneven heating and difficulty in controlling the grain structure. SUMMARY

[0004] The purpose of the present application is to solve the above problems in the prior art and provide a high-performance alloy bump material and IC packaging process. By using a special IC packaging process in combination with the selection of alloy bump and electroplated materials, the internal grain structure of the alloy bump is precisely controlled. The specific solutions are as follows:

[0005] An IC packaging process includes the following methods: detecting the internal grain structure of the alloy bump before heating treatment by using a metallographic microscope, and dividing the alloy bump into at least eight heating areas; dynamically adjusting the working parameters of the electromagnetic induction heating device according to the heating areas of the alloy bump; inputting the working parameters into the electromagnetic induction heating device; heating the alloy bump using the electromagnetic induction heating device according to the working parameters, and continuously detecting the alloy bump by using the metallographic microscope during the heating process; dynamically adjusting the working parameters of the electromagnetic induction heating device according to the detection results of the metallographic microscope; detecting the internal grain structure of the alloy bump after heating treatment by using the metallographic microscope; testing the resistivity of the alloy bump using a four-probe tester; and electroplating the alloy bump after testing.

[0006] The detection of the internal grain structure of the alloy bump before heating treatment by using the metallographic microscope and the division of the alloy bump into at least eight heating areas include: classifying the detection results of the metallographic microscope by using a periodic action algorithm, and dividing the alloy bump into at least eight heating areas according to the classification. This algorithm can more accurately identify the microstructure characteristics of the alloy bump by iteratively optimizing the cluster center, thereby achieving more precise temperature control, ensuring the uniformity and accuracy of the heating treatment, improving the heating efficiency, and improving the quality and stability of the product.

[0007] The periodic action algorithm is as follows: randomly select 8 data points as initial cluster centers; assign each data point to the nearest cluster center to form 8 clusters; recalculate the center of each cluster; repeat the above steps until the cluster centers no longer change or a predetermined number of iterations is reached, wherein assigning each data point to the nearest cluster center to form 8 clusters is calculated based on the following formula:

[0008] C j ={x i :||x i -c j ||≤||x i -c k ||for all k≠j}, where C j For clusters,

[0009] x i For data points, c j The center of each cluster is determined by the following formula: |C j | represents the number of data points in the cluster. The periodic action algorithm is used to classify the detection results of metallographic microscopes, which is faster and more accurate.

[0010] The dynamic adjustment of the operating parameters of the electromagnetic induction heating device based on the heating area of ​​the alloy bump includes: automatically identifying the heating area using a deep learning algorithm, and dynamically adjusting the electromagnetic field strength and heating time of the electromagnetic induction heating device according to the heating area. It also includes the following steps: extracting features from image data using a deep learning model to help the model identify and classify different heating areas; training the deep learning model using a labeled dataset; during training, the model learns how to predict the heating area based on the input image data and outputs the corresponding electromagnetic field strength and heating time; the model predicts the heating area based on real-time acquired image data and automatically adjusts the parameters of the electromagnetic induction heating device based on the prediction results; the algorithms for all the above steps are based on the following formula:

[0011] Among them, y i For the true value, The model predicts the value, and N is the number of samples. Through the application of deep learning algorithms, real-time optimization of the heating process is achieved.

[0012] The alloy bump is made of a high-lead alloy, which has higher welding performance and reliability.

[0013] The electroplating layer of the alloy bump is made of one of the following materials: gold-tin alloy, nickel-gold alloy, or copper-nickel-gold alloy. These alloys all have excellent electrical conductivity and welding properties, making them convenient for electroplating.

[0014] Beneficial effects: The present application realizes the accurate control of the internal grain structure of the alloy bump through the special IC packaging process, detects through the metallographic microscope during heating, is divided into 8 regions, feeds back the detection results to the electromagnetic induction heating device, ensures the uniformity of the heating treatment, makes the grain structure more uniform, such optimization not only improves the heating efficiency, but also has the effects of energy saving and emission reduction.

[0015] Through the metallographic microscope and the four-probe tester, the grain structure and the resistivity are detected, potential quality problems can be found in time, and the reliability of the product is improved.

[0016] Overall, through the accurate control of the heating treatment and the electroplating process, the resistivity of the alloy bump is effectively reduced, and the IC packaging is faster. BRIEF DESCRIPTION OF DRAWINGS

[0017] Fig. 1 is a process flow diagram of the high-performance alloy bump material and the IC packaging process;

[0018] Fig. 2 is a device structure schematic diagram of the high-performance alloy bump material and the IC packaging process. DETAILED DESCRIPTION

[0019] In order to deepen the understanding of the present application, the present application will be further described in combination with the embodiments and the drawings, and the embodiments are only used to explain the present application and do not constitute a limitation on the protection scope of the present application.

[0020] In this embodiment:

[0021] Please refer to Figs. 1-2 An IC packaging process, comprising the following methods: detecting the internal grain structure of the alloy bump before heating treatment through a metallographic microscope, and dividing the alloy bump into at least eight heating regions; dynamically adjusting the working parameters of the electromagnetic induction heating device according to the heating regions of the alloy bump; inputting the working parameters into the electromagnetic induction heating device; heating the alloy bump using the electromagnetic induction heating device according to the working parameters, and continuously detecting the alloy bump through the metallographic microscope during the heating process; dynamically adjusting the working parameters of the electromagnetic induction heating device according to the detection results of the metallographic microscope; detecting the internal grain structure of the alloy bump after heating treatment through the metallographic microscope; testing the resistivity of the alloy bump using a four-probe tester; and electroplating the alloy bump after testing, in this embodiment, eight equal heating regions are adopted, wherein the areas of the eight heating regions are equal, and the whole is in the shape of eight equal parts.

[0022] The internal grain structure of the alloy bump before heat treatment is detected by metallographic microscopy, and the alloy bump is divided into at least eight heating regions. This includes classifying the detection results of the metallographic microscopy using a periodic action algorithm, and dividing the alloy bump into at least eight heating regions according to the classification.

[0023] The periodic action algorithm is as follows: randomly select 8 data points as initial cluster centers; assign each data point to the nearest cluster center to form 8 clusters; recalculate the center of each cluster; repeat the above steps until the cluster centers no longer change or a predetermined number of iterations is reached. The assignment of each data point to the nearest cluster center to form 8 clusters is calculated based on the following formula:

[0024] C j ={x i :||x i -c j ||≤||x i -c k ||for all k≠j}, where C j For a cluster, x i For data points, c j The center of each cluster is recalculated based on the following formula: |C j | represents the number of data points in the cluster.

[0025] The method for dynamically adjusting the operating parameters of an electromagnetic induction heating device based on the heating area of ​​an alloy bump includes: automatically identifying the heating area using a deep learning algorithm, and dynamically adjusting the electromagnetic field strength and heating time of the electromagnetic induction heating device according to the heating area. It also includes the following steps: extracting features from image data using a deep learning model to help the model identify and classify different heating areas; training the deep learning model using a labeled dataset; during training, the model learns how to predict the heating area based on the input image data and outputs the corresponding electromagnetic field strength and heating time; the model predicts the heating area based on real-time acquired image data and automatically adjusts the parameters of the electromagnetic induction heating device based on the prediction results; the algorithms for all the above steps are based on the following formula:

[0026] Among them, y i For the true value, Here, N represents the model's predicted value, and N is the number of samples.

[0027] The material of the alloy bump is high-lead alloy, and the material of the electroplating layer of the alloy bump is one of gold-tin alloy, nickel-gold alloy or copper-nickel-gold alloy. In the embodiment, copper-nickel-gold alloy is used as the material of the electroplating layer. The electroplating process is as follows: the alloy bump is cleaned; the cleaned alloy bump is put into an electroplating solution containing copper-nickel-gold alloy; electricity is supplied to electroplate, the current density is controlled at 2.5 A / dm 2 , and the electroplating time is 1-2 minutes; after the electroplating is completed, the alloy bump is taken out, cleaned and dried.

[0028] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the purpose and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. An IC packaging process, characterized in that, The method includes the following steps: 1) Inspecting the internal grain structure of the alloy bump before heat treatment using a metallographic microscope, and dividing the alloy bump into at least eight heating zones; 2) Dynamically adjusting the operating parameters of the electromagnetic induction heating device according to the heating zones of the alloy bump; 3) Inputting the operating parameters into the electromagnetic induction heating device; 4) Heating the alloy bump using the electromagnetic induction heating device according to the operating parameters, and continuously inspecting the alloy bump using a metallographic microscope during the heating process; 5) Dynamically adjusting the operating parameters of the electromagnetic induction heating device based on the metallographic microscope results; 6) Inspecting the internal grain structure of the alloy bump after heat treatment using a metallographic microscope; 7) Performing resistivity testing on the alloy bump using a four-probe tester; 8) Electroplating the tested alloy bump. The step of detecting the internal grain structure of the alloy bump before heat treatment using a metallographic microscope and dividing the alloy bump into at least eight heating regions includes: classifying the detection results of the metallographic microscope using a periodic action algorithm, and dividing the alloy bump into at least eight heating regions according to the classification. Randomly select 8 data points as initial cluster centers; assign each data point to the nearest cluster center to form 8 clusters; recalculate the center of each cluster; repeat the above steps until the cluster centers no longer change or a predetermined number of iterations is reached, wherein assigning each data point to the nearest cluster center to form 8 clusters is calculated based on the following formula: ,in, For clusters, For data points, The center of each cluster is determined by the following formula: , This represents the number of data points in the cluster.

2. The IC packaging process according to claim 1, characterized in that, The method of dynamically adjusting the operating parameters of the electromagnetic induction heating device based on the heating area of ​​the alloy bump includes: automatically identifying the heating area using a deep learning algorithm, and dynamically adjusting the electromagnetic field strength and heating time of the electromagnetic induction heating device according to the heating area. It also includes the following steps: extracting features from image data using a deep learning model to help the model identify and classify different heating areas; training the deep learning model using a labeled dataset; during training, the model learns how to predict the heating area based on the input image data and outputs the corresponding electromagnetic field strength and heating time; the model predicts the heating area based on real-time acquired image data and automatically adjusts the parameters of the electromagnetic induction heating device based on the prediction results; the algorithms for all the above steps are based on the following formula: ,in, For the true value, These are the model's predicted values. This represents the number of samples.

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