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JP-7855981-B2 - Joint defect distribution estimation device, joint defect distribution estimation method, and joint defect distribution estimation program

JP7855981B2JP 7855981 B2JP7855981 B2JP 7855981B2JP-7855981-B2

Inventors

  • 臼井 正則
  • 桑原 誠
  • 村松 潤哉
  • 庄司 智幸
  • 佐藤 敏一
  • 岡地 涼輔

Assignees

  • 株式会社豊田中央研究所

Dates

Publication Date
20260511
Application Date
20220922
Priority Date
20211001

Claims (10)

  1. An acquisition unit that acquires a stress waveform distribution indicated by waveform signals output from each of the multiple stress sensors of a power module in which a chip structure having multiple stress sensors and power elements as an integrated unit and a substrate are joined by a joint, An estimation unit estimates a joint defect distribution that shows the defect distribution of the joint in contact with the chip structure, in accordance with the stress waveform distribution obtained by the acquisition unit. A device for estimating the distribution of junction defects.
  2. The bonding defect distribution estimation device according to claim 1, wherein the stress waveform distribution is indicated by the waveform signals output from each of the plurality of stress sensors when a predetermined pulse waveform is applied to the power element and current is passed through it.
  3. The chip structure includes a power element region which is the region of the power element and a stress sensor region which is the region of the stress sensor. A pulse generation circuit that generates the aforementioned pulse waveform, A gate drive circuit that applies the pulse waveform generated by the pulse generation circuit to the gate of the power element while controlling the on and off timing of the pulse waveform, A power supply for the power element that supplies power to the power element, A stress sensor power supply that supplies power to each of the aforementioned stress sensors, The bonding defect distribution estimation device according to claim 2, further comprising the above.
  4. An analog-to-digital converter that converts the waveform signals output from each of the aforementioned multiple stress sensors into digital signals, A generation unit that generates the stress waveform distribution from the digital signal converted by the analog/digital converter, The bonding defect distribution estimation device according to claim 3, further comprising the above.
  5. The system further includes a memory unit that stores a trained model generated by machine learning on a set of training data obtained by associating each of several previously acquired stress waveform distributions with each of several previously acquired joint defect distributions. The bonding defect distribution estimation device according to claim 1, wherein the estimation unit inputs the stress waveform distribution acquired by the acquisition unit to the trained model and outputs the bonding defect distribution from the trained model to estimate the bonding defect distribution corresponding to the stress waveform distribution.
  6. The power element has a rectangular surface defined by an edge extending in the X-axis direction and an edge extending in the Y-axis direction. The stress sensor is a stress sensor that has sensitivity in the X-axis direction, The bonding defect distribution estimation device according to claim 1, wherein, with the center of the power element as the origin (0,0) and one vertex as (a,a), the plurality of stress sensors are arranged such that the centroids of the plurality of stress sensors, which are arranged in a range enclosed by the four points (0,0), (a,0), (0,a), (a,a), are included in the range enclosed by the four points (0,0), (a/2,0), (0,a), (a/2,a).
  7. The plurality of stress sensors arranged in the area surrounded by the four points (0,0), (a,0), (0,a), and (a,a) are two stress sensors, The distance between the sensor's center of gravity and the origin is between 0.2a and 0.7a. The angle between the line connecting the sensor's center of gravity and the origin, and the X-axis direction, is between 22° and 90°. The distance between sensors is between 0.14a and 0.91a. The bonding defect distribution estimation device according to claim 6, wherein the angle between the line connecting the sensor positions and the X-axis direction is 22° to 90°.
  8. The plurality of stress sensors arranged in the area surrounded by the four points (0,0), (a,0), (0,a), and (a,a) are three stress sensors, The distance between the sensor's center of gravity and the origin is between 0.27a and 0.61a. The angle between the line connecting the sensor's center of gravity and the origin, and the X-axis direction, is between 27° and 81°. The bonding defect distribution estimation device according to claim 6, wherein the area enclosed by the straight line connecting the sensor positions is 0 or more and 0.16a² or less.
  9. The power module, which comprises a chip structure having multiple stress sensors and power elements integrated together, and a substrate joined by a joint, acquires the stress waveform distribution shown by the waveform signals output from each of the multiple stress sensors, Based on the acquired stress waveform distribution, a joint defect distribution showing the defect distribution of the joint in contact with the chip structure is estimated. Method for estimating the distribution of junction defects.
  10. The power module, which has a chip structure integrating multiple stress sensors and power elements, and a substrate joined by a joint, acquires the stress waveform distribution shown by the waveform signals output from each of the multiple stress sensors, In accordance with the acquired stress waveform distribution, the bonding defect distribution showing the defect distribution of the bonding portion in contact with the chip structure is estimated. A program for estimating the distribution of junction defects, designed to be run on a computer.

Description

This invention relates to a joint defect distribution estimation device, a joint defect distribution estimation method, and a joint defect distribution estimation program. For example, Patent Document 1 describes a technique for predicting failures of soldered electronic components on a printed circuit board by attaching strain gauges to the back surface of the printed circuit board. This technique detects vibrations generated during use using strain gauges and analyzes the detected vibration waveforms using machine learning. This makes it possible to determine whether or not a failure will occur within one week after the analysis period (one month). Furthermore, as shown in Figure 21, there is a power module on which power elements used in inverters and the like are mounted. Figure 21 is a cross-sectional view showing the structure of a power module 400. The power module 400 shown in Figure 21 has a substrate 42 mounted on a cooler 43. The substrate 42 is a mounting substrate, and for example, it has a structure in which copper foil (Cu) is stacked vertically with a silicon nitride film (SiN) in between. Power elements 200 are mounted on the substrate 42 via solder 41. The heat generated by the power elements 200 is removed by the cooler 43, thereby controlling the element temperature. In this type of power module 400 mounting structure, an upper limit is set for the element temperature, and the power elements 200 are operated within a range that does not exceed this upper limit. Japanese Patent Publication No. 2020-170738 This figure shows an example of the configuration of a bonding defect distribution estimation device according to an embodiment.This is a cross-sectional view showing an example of a power module according to the embodiment.This figure shows an example of a pulse waveform according to the embodiment.This figure shows an example of a stress waveform distribution according to the embodiment.This block diagram shows an example of the electrical configuration of a control device according to the embodiment.This is a block diagram showing an example of the functional configuration of a control device according to the embodiment.This figure shows an example of a junction defect distribution according to the embodiment.This figure shows an example of a binarized junction defect distribution according to the embodiment.This figure illustrates the bonding defect distribution estimation process by the estimation unit according to the embodiment.This flowchart shows an example of the processing flow by the bonding defect distribution estimation program according to the embodiment.This is a cross-sectional view showing an example of the process for creating a chip structure according to the embodiment.This is a cross-sectional view showing an example of the manufacturing process for a chip structure according to the embodiment, and is a continuation of the manufacturing process shown in Figure 11.This figure shows an example of a set of training data according to the embodiment.This is a conceptual diagram showing an example of a neural network according to the embodiment.This block diagram shows another example of the functional configuration of the control device according to the embodiment.This flowchart shows an example of the processing flow by the junction defect distribution learning program according to the embodiment.This figure shows an example of how the stress waveform changes when a junction defect occurs at the end of an element.This figure shows an example of how the temperature waveform changes when a junction defect occurs at the edge of the element.This figure shows an example of how the stress waveform changes when a junction defect occurs in the center of the element.This figure shows an example of how the temperature waveform changes when a junction defect occurs in the center of the element.This is a cross-sectional view showing the structure of the power module.This figure illustrates the effect of thermal resistance on the junction defect distribution of a power module.This is a cross-sectional view showing the structure of a power module to which strain gauges have been applied.This is an exploded view showing the structure of the power module.This diagram illustrates the range of the sensor's center of gravity.This figure shows an example of a junction defect distribution.This figure shows the stress waveform distribution in joint defect distributions 1 and 2.This figure shows the in-plane distribution of stress waveform changes with respect to the joint defect distribution.This diagram explains how to obtain the input data.This is a conceptual diagram showing an example of a neural network according to the second embodiment.This diagram illustrates the defect location, which is the output of the neural network.This is a conceptual diagram showing an example of a neural network according to the second embodiment.This figure shows an example of a junction defect distribution