A semiconductor laser defect detection method and system

Abstract

The application relates to the technical field of laser defect detection, in particular to a semiconductor laser defect detection method and system. According to the temperature change characteristics of infrared temperature images at different positions at all times, uneven positions constituting multiple thermal flow distribution uneven regions are screened out; according to the thermal resistance changes at different times on the thermal resistance time sequence curves of each thermal flow distribution uneven region, multiple thermal resistance mutation times are obtained; according to the distribution of thermal resistances in the neighborhood range of different thermal resistance mutation times, the packaging defect possibility of each thermal flow distribution uneven region is obtained, and a packaging defect region is screened out; according to the number of packaging defect regions in each semiconductor laser and the packaging defect possibility, the quality score of each semiconductor laser is obtained, and defects are detected; the application improves the accuracy of defect detection by obtaining the packaging defects and the quality score of each semiconductor laser.

Description

Technical Field

[0001] This invention relates to the field of laser defect detection technology, and specifically to a method and system for detecting defects in semiconductor lasers. Background Technology

[0002] Semiconductor lasers have a wide range of applications in industry, scientific research, military, and medical fields due to their advantages such as compact structure, easy integration, wide wavelength coverage, and low cost. In the production process, high-temperature bonding technology is usually used to effectively connect the laser chip with external circuits, optical systems, and other components to ensure that the laser can work stably in practical applications. However, temperature fluctuations during the bonding process can cause defects in the connection process, such as voids and cracks, resulting in unstable device quality. Therefore, it is necessary to perform defect detection on the bonded devices.

[0003] In existing technologies, thermal resistance testing equipment is used to monitor the heat distribution of the laser during operation, and thermodynamic analysis is used to identify structural defects in the welding process inside the device, quickly identify areas with abnormal thermal performance, and infer possible defect areas inside the laser. However, since there is hard contact between different material layers during the testing process, it can also cause changes in the local thermal resistance of the device, resulting in errors in the defect detection process. Summary of the Invention

[0004] To address the technical problem that hard contacts between different material layers can cause changes in local thermal resistance of the device, leading to errors in defect detection, this invention aims to provide a method and system for defect detection in semiconductor lasers. The specific technical solution adopted is as follows:

[0005] This invention proposes a method for detecting defects in semiconductor lasers, the method comprising:

[0006] Acquire infrared temperature images of the semiconductor laser at each moment during the cooling process after high-temperature bonding;

[0007] Based on the temperature change characteristics of infrared temperature images at different locations at all times, the heat flow distribution uniformity at each location is obtained, and the non-uniform locations that constitute multiple non-uniform heat flow distribution regions are screened out.

[0008] The thermal resistance time-series curves of each region with uneven heat flow distribution are obtained. Based on the thermal resistance changes at different times on the thermal resistance time-series curves, multiple thermal resistance abrupt change times are obtained. Based on the distribution of thermal resistance in the neighborhood of each thermal resistance abrupt change time, the peak variation factor of each thermal resistance abrupt change time is obtained. Based on the distribution of peak variation factors of different thermal resistance abrupt change times, the degree of thermal resistance fluctuation in each region with uneven heat flow distribution is obtained.

[0009] Based on the distribution of thermal resistance fluctuations, thermal resistance changes, and temperature distribution at different locations in different areas of uneven heat flow, the probability of encapsulation defects in each area of ​​uneven heat flow is obtained, and areas with encapsulation defects are screened out.

[0010] Based on the number of packaging defect areas in each semiconductor laser and the probability of packaging defects, a quality score is obtained for each semiconductor laser, and defects are detected.

[0011] Furthermore, the method for obtaining the uniformity of heat flux distribution includes:

[0012] Obtain the absolute value of the temperature slope of the infrared temperature image at each location between the previous moment and each moment, and obtain the cumulative value of the absolute value of the temperature slope of the infrared temperature image at each location between all moments, as the degree of cooling change at each location.

[0013] Obtain the average degree of temperature change at all locations as the overall degree of temperature change;

[0014] The heat flux distribution uniformity at each location is obtained by considering the difference between the degree of temperature change at each location and the overall degree of temperature change, as well as the maximum degree of temperature change. The degree of difference, the maximum degree of temperature change, and the heat flux distribution uniformity are negatively correlated.

[0015] Furthermore, the screening of non-uniform locations constituting multiple regions of non-uniform heat flow distribution includes:

[0016] If the heat flow distribution uniformity at a location is less than or equal to a preset uniformity threshold, the corresponding location will be considered a non-uniform location.

[0017] The area encompassing all adjacent locations is considered as a region of uneven heat flow distribution.

[0018] Furthermore, the method for obtaining the moment of thermal resistance abrupt change includes:

[0019] The derivative is calculated at each moment on the thermal resistance time series curve. If the derivative result is greater than or equal to the preset derivative threshold, the corresponding moment is taken as the moment of thermal resistance change.

[0020] Furthermore, the method for obtaining the peak variation factor includes:

[0021] Obtain the time difference corresponding to the decay of the peak thermal resistance to half of the peak value within the neighborhood range; obtain the thermal resistance at each thermal resistance abrupt change time and the thermal resistance difference between the minimum thermal resistance value among all thermal resistance abrupt change times.

[0022] Based on the time difference and thermal resistance difference corresponding to each thermal resistance change moment, the peak variation factor for each thermal resistance change moment is obtained. The thermal resistance difference is positively correlated with the peak variation factor, while the time difference is negatively correlated with the peak variation factor.

[0023] Furthermore, the method for obtaining the degree of thermal resistance fluctuation includes:

[0024] For any region with uneven heat flow distribution, the mean value of the peak variation factor at all times of abrupt change in thermal resistance is obtained as the average fluctuation variation factor.

[0025] The mean difference between the peak change factor and the average fluctuation change factor at all thermal resistance abrupt change moments is obtained as the first fluctuation coefficient.

[0026] The product of the first fluctuation coefficient and the average fluctuation change factor is obtained as the degree of thermal resistance fluctuation in the corresponding uneven heat flow distribution region.

[0027] Furthermore, the method for obtaining the probability of packaging defects includes:

[0028] The mean difference between the degree of thermal resistance fluctuation and the minimum degree of thermal resistance fluctuation in different regions with uneven heat flow distribution is obtained as the overall thermal resistance fluctuation deviation; the ratio of the degree of thermal resistance fluctuation in each region with uneven heat flow distribution to the overall thermal resistance fluctuation deviation is obtained as the first defect probability in each region with uneven heat flow distribution.

[0029] The average value of all derivatives is obtained by taking the derivative at each moment on the thermal resistance time series curve, which is used as the average rate of change of thermal resistance.

[0030] The ratio of the first defect probability to the average thermal resistance change rate is obtained and normalized to represent the encapsulation defect probability for each region with uneven heat flow distribution.

[0031] Furthermore, the method for obtaining the packaging defect region includes:

[0032] If the probability of a packaging defect in a region with uneven heat flow distribution is greater than or equal to a preset defect threshold, the corresponding region with uneven heat flow distribution will be designated as a packaging defect region.

[0033] Furthermore, the method for obtaining the quality score includes:

[0034] If there are no packaging defect areas, set the quality score of each semiconductor laser to a positive integer 1;

[0035] If the number of packaging defect areas is less than or equal to the preset number of areas, the sum of the probability of packaging defects in all areas with uneven heat flow distribution is obtained, and the product of the sum and the preset weight is calculated as the quality score of each semiconductor laser.

[0036] If the number of defective packaging areas exceeds the preset number, the quality score for each semiconductor laser will be set to 0.

[0037] The present invention also proposes a semiconductor laser defect detection system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of any one of the semiconductor laser defect detection methods.

[0038] The present invention has the following beneficial effects:

[0039] This invention, based on the temperature change characteristics of infrared temperature images at different locations at all times, filters out the non-uniform locations constituting multiple regions of non-uniform heat flow distribution, and performs targeted analysis on key non-uniform regions. Based on the thermal resistance changes at different times on the thermal resistance time-series curve of each non-uniform heat flow distribution region, it obtains multiple moments of thermal resistance abrupt change, accurately capturing the specific moments when these structures change. Based on the distribution of thermal resistance within the neighborhood of each thermal resistance abrupt change moment, it obtains the probability of packaging defects in each non-uniform heat flow distribution region, filtering out packaging defect regions, which helps to accurately assess the quality of subsequent devices. Based on the number of packaging defect regions in each semiconductor laser and the probability of packaging defects, it obtains a quality score for each semiconductor laser, achieving a systematic and refined packaging quality assessment in both breadth and depth, and detecting defects. This invention improves the accuracy of defect detection by obtaining the packaging defects and quality scores for each semiconductor laser. Attached Figure Description

[0040] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A flowchart illustrating a semiconductor laser defect detection method provided in one embodiment of the present invention;

[0042] Figure 2 This is a flowchart of a method for obtaining heat flow uniformity according to an embodiment of the present invention. Detailed Implementation

[0043] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a semiconductor laser defect detection method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0045] The following description, in conjunction with the accompanying drawings, details the specific scheme of the semiconductor laser defect detection method and system provided by the present invention.

[0046] Please see Figure 1 The diagram illustrates a flowchart of a semiconductor laser defect detection method according to an embodiment of the present invention, specifically including:

[0047] Step S1: Obtain the infrared temperature image of the semiconductor laser at each moment during the cooling process after high-temperature bonding.

[0048] In the embodiments of the present invention, considering that there is hard contact between different material layers, resulting in local thermal resistance changes and fluctuations in the structure function curve, the more interference there is in the detection of defects, the more necessary it is to combine and analyze the relationship between temperature change and thermal resistance change. First, an infrared thermal imager is used to obtain the real-time temperature distribution on the surface of the semiconductor laser, and the infrared temperature image of the semiconductor laser at each moment during the cooling process after high-temperature bonding is obtained, showing the temperature changes at different locations on the image.

[0049] It should be noted that, in one embodiment of the present invention, the sampling frequency is 1 minute, that is, the interval between moments is 1 minute; the acquisition time range is the process of the device cooling to room temperature after high-temperature bonding and packaging; in other embodiments of the present invention, the acquisition situation can be set according to specific circumstances, which will not be limited or described in detail here.

[0050] Step S2: Based on the temperature change characteristics of infrared temperature images at different locations at all times, obtain the heat flow distribution uniformity at each location, and filter out the non-uniform locations that constitute multiple non-uniform heat flow distribution regions.

[0051] Analyzing the temperature change characteristics at different times helps to understand the rate of temperature change. The greater the rate of temperature change, the greater the degree of cooling change between two times. The temperature is highest at the seam of different material layers and gradually decreases with heat transfer. Defects that lead to poor packaging may affect the heat conduction inside the laser, increasing local heat accumulation and reducing heat flow uniformity. Based on the temperature change characteristics of infrared temperature images at different locations at all times, the heat flow distribution uniformity at each location can be obtained.

[0052] Preferably, in one embodiment of the present invention, the method for obtaining heat flow uniformity is described in [reference needed]. Figure 2 It shows a flowchart of a method for obtaining heat flux uniformity, including:

[0053] Step S201: Obtain the absolute value of the temperature slope of the infrared temperature image at each location between the previous moment and each moment, and obtain the cumulative value of the absolute value of the temperature slope of the infrared temperature image at each location between all moments, as the degree of cooling change at each location.

[0054] The temperature slope reflects the rate of temperature change. The larger the absolute value of the temperature slope, the faster the rate of temperature change and the greater the degree of cooling.

[0055] It should be noted that, in one embodiment of the present invention, the temperature slope is calculated as the ratio of the temperature difference between the previous moment and each moment at each location to the corresponding moment difference. Since it is a cooling process, the temperature between the previous moment and each moment is not negative, and the resulting slope is negative. Therefore, the absolute value of the temperature slope is subsequently processed. In other embodiments of the present invention, temperature curves of infrared temperature images at each location at all moments can be constructed, and the derivative of the temperature curve at each moment can be obtained as the slope. The specific means are well known to those skilled in the art and will not be described in detail here.

[0056] Step S202: Obtain the average degree of cooling change at all locations as the overall degree of cooling change.

[0057] The overall trend of temperature change at all locations is quantified by averaging, and the overall degree of temperature change is obtained for subsequent data comparison.

[0058] Step S203: Based on the degree difference between the temperature change at each location and the overall degree of temperature change, as well as the maximum value of the temperature change, obtain the heat flow distribution uniformity at each location. The degree difference, the maximum value of the temperature change, and the heat flow distribution uniformity are negatively correlated.

[0059] It should be noted that the difference between the degree of temperature change and the overall degree of temperature change reflects the temperature change at each location relative to the overall location. The smaller the difference in degree, the closer the temperature changes are, the more consistent the heat flow changes are, and the greater the uniformity of heat flow distribution. The maximum value of the degree of temperature change reflects the rate characteristics of heat flow transfer; the greater the degree of temperature change, the faster the heat flow transfer, and the smaller the uniformity of heat flow distribution. Therefore, the degree difference and the maximum value of the degree of temperature change are negatively correlated with the uniformity of heat flow distribution.

[0060] In one embodiment of the present invention, the product of the degree difference between the degree of cooling change at each location and the overall degree of cooling change and the degree of cooling change is calculated, and a negative correlation mapping is performed as the heat flow distribution uniformity at each location; therefore, based on the above basic mathematical elements, a correlation relationship is constructed between the degree difference, the degree of cooling change and the heat flow distribution uniformity, that is, the greater the degree difference, the smaller the degree of cooling change and the greater the heat flow distribution uniformity.

[0061] It should be noted that, in the embodiments of the present invention, the difference represents the absolute value of the calculated difference; this is achieved by taking the reciprocal or by using an exponential function with the natural constant as the base. When performing negative correlation mapping, in order to avoid the difference in the denominator being 0 and the formula being meaningless, a manually set threshold, such as 0.01, is added when calculating the reciprocal. The specific means are well known to those skilled in the art and will not be described in detail here.

[0062] Preferably, the uniformity of heat flow distribution reflects the synchronicity and consistency of heat dissipation in space. The greater the uniformity of heat flow distribution, the more uniform the heat dissipation capacity of the laser, and the more synchronous the temperature change rate at each location during the cooling process. In one embodiment of the present invention, the method for selecting the non-uniform locations constituting multiple non-uniform heat flow distribution regions includes:

[0063] If the heat flow distribution uniformity at a location is less than or equal to a preset uniformity threshold, the corresponding location will be considered a non-uniform location.

[0064] The area encompassing all adjacent locations is considered as a region of uneven heat flow distribution.

[0065] It should be noted that, in one embodiment of the present invention, the preset uniform threshold is set to 0.5; in other embodiments of the present invention, the preset uniform threshold can be set according to specific circumstances, and will not be limited or elaborated here.

[0066] Step S3: Obtain the thermal resistance time series curve for each region with uneven heat flow distribution; obtain multiple thermal resistance abrupt change moments based on the thermal resistance changes at different times on the thermal resistance time series curve; obtain the probability of encapsulation defects in each region with uneven heat flow distribution based on the thermal resistance fluctuation distribution, thermal resistance changes, and thickness differences between laser material layers, and screen out the encapsulation defect regions; obtain the degree of thermal resistance fluctuation in each region with uneven heat flow distribution based on the peak change factor distribution at different thermal resistance abrupt change moments.

[0067] The thermal field of a laser changes dynamically over time. To analyze the changes in thermal performance more comprehensively, the thermal resistance time-series curves for each region with uneven heat flow distribution are obtained. It should be noted that, based on the thermodynamic principle of the heat balance equation, known parameters such as temperature, heat flow, and heat capacity are obtained through pre-measurement by the implementers. The thermal resistance of each region with uneven heat flow distribution at each moment is then calculated, and the curves obtained by fitting the thermal resistance of each region with uneven heat flow distribution at all moments are used as the thermal resistance time-series curves. Existing fitting methods such as least squares or polynomial fitting can be used for fitting; the specific methods are well-known to those skilled in the art and will not be elaborated here.

[0068] As the stress generated by temperature changes varies, creep and plastic deformation occur between layers during the encapsulation process, leading to defects such as cracks and bubbles. The thermal resistance changes in the region during the cooling process show significant abrupt changes. Based on the thermal resistance time sequence, multiple abrupt changes in thermal resistance are obtained at different times on the curve.

[0069] Preferably, in one embodiment of the present invention, the method for obtaining the moment of thermal resistance abrupt change includes:

[0070] The derivative is calculated at each moment on the thermal resistance time series curve. If the derivative result is greater than or equal to the preset derivative threshold, the corresponding moment is taken as the moment of thermal resistance change.

[0071] It should be noted that, in one embodiment of the present invention, the preset derivative threshold is 3.5; in other embodiments of the present invention, the preset derivative threshold can be set according to specific circumstances, and will not be limited or elaborated here.

[0072] Since thermal resistance changes manifest as small, relatively smooth fluctuations in a localized area, and these peaks have small amplitudes and long durations, a targeted analysis of the distribution of thermal resistance within the neighborhood is conducted to quantify the peak variation factor. Based on the distribution of thermal resistance within the neighborhood at each thermal resistance abrupt change moment, the peak variation factor at each thermal resistance abrupt change moment is obtained.

[0073] Preferably, in one embodiment of the present invention, the method for obtaining the peak variation factor includes:

[0074] Obtain the time difference corresponding to the decay of the peak thermal resistance to half of the peak value within the neighborhood range; obtain the thermal resistance at each thermal resistance abrupt change time and the thermal resistance difference between the minimum thermal resistance value among all thermal resistance abrupt change times.

[0075] It should be noted that, in one embodiment of the present invention, the method for obtaining the neighborhood range includes: obtaining the target time when the thermal resistance value is a preset multiple of the thermal resistance value corresponding to each thermal resistance change time, and taking the time range formed between the thermal resistance change time and the adjacent target time as the neighborhood range; wherein, the preset multiple is 10%; in other embodiments of the present invention, the size of the neighborhood range can be specifically set according to the specific situation, and is not limited or described here.

[0076] Based on the time difference and thermal resistance difference corresponding to each thermal resistance change moment, the peak variation factor for each thermal resistance change moment is obtained. The thermal resistance difference is positively correlated with the peak variation factor, while the time difference is negatively correlated with the peak variation factor.

[0077] It should be noted that the thermal resistance difference reflects the change in the maximum thermal resistance at each moment of thermal resistance change from the minimum thermal resistance in the whole. The larger the thermal resistance difference, the greater the degree of position change and the larger the peak change factor. The smaller the time difference, the less time is used for thermal resistance change and the larger the peak change factor. Therefore, the thermal resistance difference is positively correlated with the peak change factor, and the time difference is negatively correlated with the peak change factor.

[0078] In one embodiment of the present invention, the ratio of thermal resistance difference to time difference is calculated as the peak variation factor; thus, a correlation is established between time difference, thermal resistance difference and peak variation factor, that is, the larger the time difference, the smaller the thermal resistance difference and the smaller the peak variation factor; wherein, in order to avoid the denominator of the formula being 0 and the formula being meaningless when calculating the ratio, an artificially set threshold, such as 0.01, is added to the denominator. The specific means are well known to those skilled in the art and will not be described in detail here.

[0079] Abnormal fluctuations caused by material inhomogeneity, adhesive layer defects, and microcracks can exist in various areas of the encapsulation structure. These defects usually appear in different locations, and due to the variable size of the defects, the larger the location change, the more chaotic the peak changes. Based on the distribution of peak change factors at different thermal resistance change moments, the degree of thermal resistance fluctuation in each region with uneven heat flow distribution can be obtained.

[0080] Preferably, in one embodiment of the present invention, the method for obtaining the degree of thermal resistance fluctuation includes:

[0081] For any region with uneven heat flow distribution, the mean value of the peak variation factor at all times of abrupt change in thermal resistance is obtained as the average fluctuation variation factor.

[0082] The mean difference between the peak change factor and the average fluctuation change factor at all thermal resistance abrupt change moments is obtained as the first fluctuation coefficient.

[0083] The product of the first fluctuation coefficient and the average fluctuation change factor is obtained as the degree of thermal resistance fluctuation in the corresponding uneven heat flow distribution region.

[0084] Step S4: Based on the distribution of thermal resistance fluctuations and changes in thermal resistance in different areas of uneven heat flow distribution, obtain the probability of encapsulation defects in each area of ​​uneven heat flow distribution, and screen out areas with encapsulation defects.

[0085] Preferably, in one embodiment of the present invention, the method for obtaining the probability of packaging defects includes:

[0086] The mean difference between the degree of thermal resistance fluctuation and the minimum degree of thermal resistance fluctuation in different regions with uneven heat flow distribution is obtained as the overall thermal resistance fluctuation deviation; the ratio of the degree of thermal resistance fluctuation in each region with uneven heat flow distribution to the overall thermal resistance fluctuation deviation is obtained as the first defect probability in each region with uneven heat flow distribution.

[0087] The average value of all derivatives is obtained by taking the derivative at each moment on the thermal resistance time series curve, which is used as the average rate of change of thermal resistance.

[0088] The ratio of the first defect probability to the average thermal resistance change rate is obtained and normalized to represent the encapsulation defect probability for each region with uneven heat flow distribution.

[0089] It should be noted that, considering the differences in thermal resistance between different material layers or the unevenness of the material layers leading to material bending and deformation during cooling, which can then form cracks or bubbles, encapsulation defects often manifest in areas with significant differences in the thickness of the material layers. In another embodiment of the present invention, the thickness difference between two adjacent material layers in areas of uneven heat flow distribution is analyzed, and the method for obtaining this information is as follows:

[0090] In regions with uneven heat flow distribution, multiple straight lines are drawn along the vertical bonding interface on the infrared temperature image. The temperature gradient at each location on the line is calculated. If the temperature gradient at a certain location on either side of the region is greater than the temperature gradients on the adjacent sides, the corresponding location is designated as a temperature abrupt change point. The difference in relative distance between different temperature abrupt change points and the center of the uneven heat flow distribution region is obtained as the thickness difference between two adjacent material layers in the uneven heat flow distribution region. The product of the thickness difference and the ratio of the first defect probability to the average thermal resistance change rate is calculated and normalized to represent the probability of encapsulation defects, thus more accurately analyzing the defect situation in the uneven heat flow distribution region. The vertical bonding interface is the direction in which heat is transferred from the laser chip to the heat sink, reflecting the temperature information of the internal interface condition.

[0091] The probability of a packaging defect reflects the severity of each defective area. The higher the probability of a packaging defect, the more severe the defective area is, thus filtering out the packaging defective areas.

[0092] Preferably, in one embodiment of the present invention, the method for obtaining the packaging defect region includes:

[0093] If the probability of a packaging defect in a region with uneven heat flow distribution is greater than or equal to a preset defect threshold, the corresponding region with uneven heat flow distribution will be designated as a packaging defect region.

[0094] It should be noted that, in one embodiment of the present invention, the preset defect threshold is set to 0.68; in other embodiments of the present invention, the preset defect threshold may be set according to specific circumstances, which will not be elaborated here.

[0095] Step S5: Based on the number of packaging defect areas in each semiconductor laser and the probability of packaging defects, obtain a quality score for each semiconductor laser and detect the defects.

[0096] The more packaging defect areas there are, the greater the probability of device failure under thermal stress, the greater the likelihood of packaging defects, the greater the severity of the defect areas, and the more likely quality problems will occur. Combining the number of packaging defect areas and the likelihood of packaging defects to quantify the quality score achieves a systematic and refined packaging quality assessment in both breadth and depth. Based on the number of packaging defect areas and the likelihood of packaging defects in each semiconductor laser, a quality score is obtained for each semiconductor laser, and defects are detected.

[0097] Preferably, in one embodiment of the present invention, the method for obtaining the quality score includes:

[0098] If there are no packaging defect areas, set the quality score of each semiconductor laser to a positive integer 1;

[0099] If the number of packaging defect areas is less than or equal to the preset number of areas, the sum of the probability of packaging defects in all areas with uneven heat flow distribution is obtained, and the product of the sum and the preset weight is calculated as the quality score of each semiconductor laser.

[0100] If the number of defective packaging areas exceeds the preset number, the quality score for each semiconductor laser will be set to 0.

[0101] It should be noted that, in one embodiment of the present invention, the number of preset regions is set to 3, and the preset weight is used to reflect the impact of defective regions in the laser on the device quality score. Considering that the more defective regions there are, the worse the device quality may be, and the greater the impact, the preset weight is set to 0.8, which increases the confidence of the possibility of packaging defects. In other embodiments of the present invention, the preset weight and the number of preset regions can be set according to specific circumstances, and are not limited or elaborated here.

[0102] It should be noted that, in another embodiment of the present invention, after obtaining the quality score of the semiconductor laser, if the quality scores of multiple consecutive lasers are less than or equal to the preset evaluation threshold, it indicates that there is thermal stress concentration in the high-temperature bonding process, resulting in packaging defects. It is necessary to feed back the abnormal batch information to the process engineers and equipment maintenance personnel to investigate and repair the production line, confirm whether there are any production process technology problems, and help to achieve refined and intelligent defect detection.

[0103] It should be noted that, in one embodiment of the present invention, the preset evaluation threshold is set to 0 or 5; in other embodiments of the present invention, the preset evaluation threshold may be set according to specific circumstances, and will not be limited or elaborated here.

[0104] In summary, this invention, based on the temperature change characteristics of infrared temperature images at different locations at all times, filters out the non-uniform locations constituting multiple regions of non-uniform heat flow distribution; based on the thermal resistance changes at different times on the thermal resistance time-series curve of each region of non-uniform heat flow distribution, it obtains multiple moments of thermal resistance abrupt change; based on the distribution of thermal resistance in the neighborhood of each moment of thermal resistance abrupt change, it obtains the probability of packaging defects in different regions of non-uniform heat flow distribution, and filters out regions of packaging defects; based on the number of regions of packaging defects in each semiconductor laser and the probability of packaging defects, it obtains a quality score for each semiconductor laser, and detects defects; this invention improves the accuracy of defect detection by obtaining the packaging defects and quality scores of each semiconductor laser.

[0105] The present invention also proposes a semiconductor laser defect detection system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the steps of a semiconductor laser defect detection method.

[0106] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0107] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for detecting defects in a semiconductor laser, characterized in that, The method includes: Acquire infrared temperature images of the semiconductor laser at each moment during the cooling process after high-temperature bonding; Based on the temperature change characteristics of infrared temperature images at different locations at all times, the heat flow distribution uniformity at each location is obtained, and the non-uniform locations that constitute multiple non-uniform heat flow distribution regions are screened out. The thermal resistance time-series curves of each region with uneven heat flow distribution are obtained. Based on the thermal resistance changes at different times on the thermal resistance time-series curves, multiple thermal resistance abrupt change times are obtained. Based on the distribution of thermal resistance in the neighborhood of each thermal resistance abrupt change time, the peak variation factor of each thermal resistance abrupt change time is obtained. Based on the distribution of peak variation factors of different thermal resistance abrupt change times, the degree of thermal resistance fluctuation in each region with uneven heat flow distribution is obtained. Based on the distribution of thermal resistance fluctuations and thermal resistance changes in different uneven heat flow distribution areas, the probability of encapsulation defects in each uneven heat flow distribution area is obtained, and the encapsulation defect areas are screened out. Based on the number of packaging defect regions in each semiconductor laser and the probability of packaging defects, a quality score is obtained for each semiconductor laser, and defects are detected. The method for obtaining the peak variation factor includes: Obtain the time difference corresponding to the decay of the peak thermal resistance to half of the peak value within the neighborhood range; obtain the thermal resistance at each thermal resistance abrupt change time and the thermal resistance difference between the minimum thermal resistance value among all thermal resistance abrupt change times. Based on the time difference and thermal resistance difference corresponding to each thermal resistance change moment, the peak change factor for each thermal resistance change moment is obtained. The thermal resistance difference is positively correlated with the peak change factor, and the time difference is negatively correlated with the peak change factor. The method for obtaining the degree of thermal resistance fluctuation includes: For any region with uneven heat flow distribution, the mean value of the peak variation factor at all times of abrupt change in thermal resistance is obtained as the average fluctuation variation factor. The mean difference between the peak change factor and the average fluctuation change factor at all thermal resistance abrupt change moments is obtained as the first fluctuation coefficient. The product of the first fluctuation coefficient and the average fluctuation change factor is obtained as the degree of thermal resistance fluctuation in the corresponding uneven heat flow distribution region. The method for obtaining the probability of packaging defects includes: The mean difference between the degree of thermal resistance fluctuation and the minimum degree of thermal resistance fluctuation in different regions with uneven heat flow distribution is obtained as the overall thermal resistance fluctuation deviation; the ratio of the degree of thermal resistance fluctuation in each region with uneven heat flow distribution to the overall thermal resistance fluctuation deviation is obtained as the first defect probability in each region with uneven heat flow distribution. The average value of all derivatives is obtained by taking the derivative at each moment on the thermal resistance time series curve, which is used as the average rate of change of thermal resistance. The ratio of the first defect probability to the average thermal resistance change rate is obtained and normalized to represent the encapsulation defect probability for each region with uneven heat flow distribution.

2. The semiconductor laser defect detection method according to claim 1, characterized in that, The method for obtaining the uniformity of heat flux distribution includes: Obtain the absolute value of the temperature slope of the infrared temperature image at each location between the previous moment and each moment, and obtain the cumulative value of the absolute value of the temperature slope of the infrared temperature image at each location between all moments, as the degree of cooling change at each location. Obtain the average degree of temperature change at all locations as the overall degree of temperature change; The heat flux distribution uniformity at each location is obtained by considering the difference between the degree of temperature change at each location and the overall degree of temperature change, as well as the maximum degree of temperature change. The degree of difference, the maximum degree of temperature change, and the heat flux distribution uniformity are negatively correlated.

3. The semiconductor laser defect detection method according to claim 1, characterized in that, The screening process identifies non-uniform locations that constitute multiple regions of uneven heat flow distribution, including: If the heat flow distribution uniformity at a location is less than or equal to a preset uniformity threshold, the corresponding location will be considered a non-uniform location. The area encompassing all adjacent locations is considered as a region of uneven heat flow distribution.

4. The semiconductor laser defect detection method according to claim 1, characterized in that, The method for obtaining the moment of thermal resistance abrupt change includes: The derivative is calculated at each moment on the thermal resistance time series curve. If the derivative result is greater than or equal to the preset derivative threshold, the corresponding moment is taken as the moment of thermal resistance change.

5. The semiconductor laser defect detection method according to claim 1, characterized in that, The method for obtaining the packaging defect region includes: If the probability of a packaging defect in a region with uneven heat flow distribution is greater than or equal to a preset defect threshold, the corresponding region with uneven heat flow distribution will be designated as a packaging defect region.

6. The semiconductor laser defect detection method according to claim 1, characterized in that, The method for obtaining the quality score includes: If there are no packaging defect areas, set the quality score of each semiconductor laser to a positive integer 1; If the number of packaging defect areas is less than or equal to the preset number of areas, the sum of the probability of packaging defects in all areas with uneven heat flow distribution is obtained, and the product of the sum and the preset weight is calculated as the quality score of each semiconductor laser. If the number of defective packaging areas exceeds the preset number, the quality score for each semiconductor laser will be set to 0.

7. A semiconductor laser defect detection system, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the semiconductor laser defect detection method as described in any one of claims 1 to 6.

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