Stress detection method for integrated circuit chip

Abstract

The invention discloses a stress detection method for an integrated circuit chip. An initial signal is generated according to data acquired by a sensing element; the generated initial signals are converted into digital information, conversion from analog signals to discrete numerical value representation is completed through an analog-to-digital converter, and a data set representing the relationship between time and stress change is constructed according to discrete numerical values; generating a mapping chart for visually displaying the stress distribution dynamic state by using the formed data set, analyzing the mapping chart, determining the position where an abnormal mode occurs in the chart, and starting a targeted test program for a delimited problem area to confirm that an abnormal stress condition actually exists; and outputting alarm information to a monitoring system, and indicating the specifically positioned abnormal stress area. According to the method, the timeliness and the accuracy of stress monitoring can be remarkably improved, and any stress change threatening the performance of the chip can be captured in the first time, so that the stable operation and the reliability of the chip in the whole life cycle are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of stress detection, and in particular relates to a stress detection method for an integrated circuit chip. Background Art

[0002] In the design and manufacturing process of integrated circuit (IC) chips, stress management is a crucial link. In the existing technology, offline detection methods are usually used to evaluate the stress status of the chip. These methods mainly include: Static stress testing: Estimate the internal stress distribution by physically measuring the chip at a specific time point or observing the surface morphology changes using tools such as microscopes. Although this method can provide certain stress information, it cannot achieve real-time monitoring.

[0003] Finite element analysis (FEA) simulation: Computer simulation software is used to predict the stress distribution of the chip under different operating conditions. However, this type of simulation relies on precise material parameters and boundary conditions, and it is difficult to capture the dynamic stress changes in actual operation.

[0004] Sensor integration: Some advanced solutions embed dedicated stress sensors during the chip design phase to collect stress data. Although this method can achieve online monitoring to a certain extent, the data processing capabilities and real-time response speed of traditional sensors are limited, resulting in inaccurate and in-time warning of stress anomalies.

[0005] A major problem with existing technologies is the lack of effective real-time stress monitoring methods, especially during normal working conditions of the chip. Since most existing methods focus on stress assessment under static or preset conditions, they cannot timely detect and locate transient stress anomalies generated under dynamic working conditions. This lag results in potential stress problems not being handled in a timely manner, which in turn affects the reliability and life of the chip. Summary of the invention

[0006] The purpose of the present invention is to provide an integrated circuit chip stress detection method, which can significantly improve the timeliness and accuracy of stress monitoring, ensure that any stress changes that threaten chip performance are captured at the first time, thereby ensuring the stable operation and reliability of the chip throughout its life cycle, so as to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides a stress detection method for an integrated circuit chip, comprising: Deploy a sensing element at a preset position inside the integrated circuit to collect stress data, and generate an initial signal based on the data obtained by the sensing element; The generated initial signal is converted into digital information, and the conversion from analog signal to discrete numerical representation is completed through an analog-to-digital converter. According to the discrete numerical values, a data set representing the relationship between time and stress change is constructed; Using the formed data set, a mapping chart is generated to intuitively display the dynamics of stress distribution, and the mapping chart is analyzed to determine the location of abnormal patterns in the chart; Based on the identified abnormal pattern location, the problematic area is delineated, and targeted testing procedures are initiated for the delineated problematic area to confirm the actual existence of abnormal stress conditions; After the test, an alarm message is output to the monitoring system, indicating the specific location of the abnormal stress area.

[0008] Preferably, the step of disposing a sensing element at a preset position inside the integrated circuit to collect stress data includes: When the sensing element is activated, an initial electrical signal U is generated. The initial electrical signal U is proportional to the stress level L, and the expression is U=k*L; Apply linear transformation to the original signal U to generate a standardized signal G, the formula is G=a*U+b; Based on the normalized signal G, calculate the average stress level M using M = sum(Gi) / n; When M is greater than the preset threshold Th, the subsequent response action is triggered, and the condition is M>Th.

[0009] Preferably, the step of generating an initial signal based on data acquired by the sensing element comprises: Reading the original electrical signal X output by the sensing element, the strength of the original electrical signal X is associated with the local stress condition; Apply a linear transformation to the original electrical signal X to generate an adjusted signal Y, calculated by Y=c*X+d; Construct a signal strength distribution map D, where the signal strength distribution map D displays Y values ​​corresponding to different positions; Calculate the relative stress index Z of each deployment point using Z=sum(Yi^2) / m.

[0010] Preferably, converting the generated initial signal into digital information comprises: Receive the adjusted signal Y and perform sampling to generate samples Us at discrete time points. The sampling interval is denoted by s. Apply the quantization process to form a value Vs. The formula Vs=round(Us / q)*q, q is the quantization step size. Encode the value Vs and construct a binary sequence Ws, ensuring that Ws=binary(Vs).

[0011] Preferably, constructing a data set representing the relationship between time and stress change according to discrete values ​​includes: Collect the binary sequence Ws, convert Ws back to the quantized stress value Vs using the inverse encoding process Vs = decode(Ws); Combine with the sampling time ts to form the time-stress pair TS, with the expression TS = (ts, Vs); Summarize all the time-stress pairs TS, establish the record set Rs, arrange them in chronological order to ensure Rs = {TS1, TS2,..., TSn}.

[0012] Preferably, using the formed data set, generate a mapping chart that intuitively shows the dynamic stress distribution, including: Read the data record set Rs, calculate the stress change amplitude ΔL at each deployment point at different time points, using ΔL = Lt - L(t - 1); Create a color coding rule Cc, map the stress change to the visual color range, with the expression Cc = colormap(ΔL); Draw the mapping chart M, integrate the color information of all deployment points, ensure M = plot(T, P, Cc), where T is the time axis and P is the position coordinates of the deployment point.

[0013] Preferably, parse the mapping chart to determine the positions where abnormal patterns appear in the chart, including: Analyze the color distribution in the mapping chart M, calculate the color deviation degree Dp of each deployment point P, with the formula Dp(P) = |Cc(P) - C_avg|, where Cc(P) is the color coding value of the deployment point P and C_avg is the average value of the color coding values of all deployment points; Establish a threshold Th to distinguish normal and abnormal color changes, with the expression Th = median(Dp) + k * IQR(Dp); Screen out the deployment points P that exceed the Th range to form the suspected abnormal set Ep, with the condition Ep = {P|Dp(P) > Th}; Perform a spatial correlation assessment on the members within Ep. If it satisfies F(P1, P2) = dist(P1, P2) < d_max and P1, P2 ∈ Ep, then mark it as the abnormal pattern area Ra.

[0014] Preferably, based on the identified positions of abnormal patterns, delimit the scope of the problematic area, including: Obtain the marked abnormal pattern area Ra; For each abnormal position Ra, determine its neighborhood Na, defined as the set consisting of all other deployment points whose distance from Ra does not exceed r, with the expression Na = {Q|dist(Ra, Q) ≤ r}; Calculate the color-coded average value Avg_Cc of all deployment points in each neighborhood Na, using Avg_Cc(Na)=sum(Cc(Q)) / count(Q); Compare the neighborhood average color code Avg_Cc with the overall average color code C_avg. When the difference ΔCc=|Avg_Cc-C_avg| exceeds the set threshold Th, the neighborhood Na is marked as a potential problem area Pa, and the conditional expression is Pa={Na|ΔCc>Th}.

[0015] Preferably, the step of initiating a targeted testing procedure for the identified problem area to confirm the actual existence of abnormal stress conditions includes: Select the marked potential problem area Pa; For each target area Pa, deploy a sensing element Hs and record the newly generated signal strength S_new to ensure that S_new = measure(Hs); Compare the new signal strength S_new with the original signal strength S_original, and evaluate the stress change by Ratio=S_new / S_original; If Ratio>Limit, it is confirmed that there is an actual abnormal stress condition in the area, and this area is included in the final abnormal list La. The conditional expression is La={Pa|Ratio>Limit}.

[0016] Preferably, after the test, an alarm message is output to the monitoring system to indicate the specific location of the abnormal stress area, including: Collect the confirmed final exception list La; For each abnormal area Pa, an alarm data packet Ba is constructed, including the position coordinates P, stress change ratio Ratio and timestamp Ts, and the expression Ba=(P, Ratio, Ts); Convert Ba into the format Fa that can be parsed by the monitoring system, using Fa=encode(Ba); Send the converted alarm information Fa, and record the sending status Sa at the same time. Use ACK to verify the successful transmission. The conditional expression is Sa=send(Fa) and check(ACK). If ACK==True, it means that the alarm has been successfully transmitted.

[0017] Technical effects and advantages of the present invention: Compared with the prior art, the present invention provides a method for detecting stress of an integrated circuit chip, which has the following advantages: This method can monitor the stress of integrated circuit chips in real time while they are in operation, and has the ability to automatically identify and accurately locate abnormal stress areas. By introducing highly sensitive sensing elements, efficient signal processing mechanisms, and intelligent analysis algorithms, this method can significantly improve the timeliness and accuracy of stress monitoring, ensuring that any stress changes that threaten chip performance are captured at the first opportunity, thereby ensuring the stable operation and reliability of the chip throughout its life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The figure is a flow chart of the integrated circuit chip stress detection method of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] The present invention provides a method for detecting stress of an integrated circuit chip. Figure 1 As shown, the following steps are included: The first step is to deploy the sensing element (piezoresistive sensor) at a preset position inside the integrated circuit (key node or place where stress concentration is likely to occur) to collect stress data, including: When the sensing element is activated, an initial electrical signal U is generated. The initial electrical signal U is proportional to the stress level L, and the expression is U=k*L, where k is a proportional factor, which is used to represent the proportional relationship between the initial electrical signal U and the stress level L. A linear transformation is applied to the original signal U to generate a standardized signal G, and the formula is G=a*U+b, where a is a proportional factor used to adjust the amplitude of the original signal U, and b is an offset used to numerically shift the standardized signal G. Based on the standardized signal G, the average stress level M is calculated, using M=sum(Gi) / n, where n represents the number of samples of the standardized signal G used to calculate the average stress level M, and Gi represents the i-th standardized signal value. When M is greater than a preset threshold Th, a subsequent response action is triggered, and the condition is M>Th.

[0021] Embodiment 1

[0022] Assumptions: Coefficient k = 0.5 (proportional factor between stress level L and initial electrical signal U); Coefficient a = 2.0 (scaling factor for linear conversion of the original signal U to the standardized signal G); Offset b = 1.0 (offset used to linearly convert the original signal U to the standardized signal G); The preset threshold value Th=3.5 (the response is triggered when the average stress level M exceeds this value); Assume that the sensing element detects a set of stress data in the activated state, corresponding to the following stress levels L: L = [2, 4, 6, 8]; According to the expression U=k*L, the corresponding initial electrical signal U is calculated as: U=[0.52,0.54,0.56,0.5*8]=[1,2,3,4]; Then, a linear transformation is applied to the original signal U to generate a standardized signal G, using the formula G=aU+b: G=[21+1,22+1,23+1,2*4+1]=[3,5,7,9]; Next, based on the standardized signal G, the average stress level M is calculated: M = sum(G) / n = (3+5+7+9) / 4 = 24 / 4 = 6; Finally, the average stress level M is compared with the preset threshold Th: M>Th=6>3.5; Because the average stress level M is greater than the preset threshold Th, the subsequent response action is triggered.

[0023] In this embodiment, through the above process, the stress changes inside the integrated circuit can be monitored in real time, and the situation when the stress level exceeds the safe range can be automatically identified. This method ensures that measures can be taken in time to investigate and deal with potential problems, which helps prevent failures caused by stress and improve the reliability and life of the chip.

[0024] The second step is to generate an initial signal based on the data obtained by the sensing element; specifically, it includes: The original electrical signal X output by the sensing element is read, and the strength of the original electrical signal X is associated with the local stress condition; a linear transformation is applied to the original electrical signal X to generate an adjusted signal Y, which is calculated by Y=c*X+d, where c is a proportional factor used to adjust the amplitude of the original electrical signal X, and d is an offset used to numerically translate the adjusted signal Y; a signal strength distribution map D is constructed, and the signal strength distribution map D shows the Y values ​​corresponding to different positions; the relative stress index Z of each deployment point is calculated, using Z=sum(Yi^2) / m, where Yi represents the adjusted signal value output by the i-th sensing element at a certain moment, and m represents the number of adjusted signal values ​​involved in calculating the relative stress index Z.

[0025] Embodiment 2

[0026] Assumptions: Coefficient c = 0.8 (scaling factor for linear transformation of original signal X to adjusted signal Y); Offset d = -2.0 (offset used to linearly transform the original signal X to the adjusted signal Y); The number of sensing elements m = 4 (used to calculate the relative stress index Z); Assume there are four sensing elements, and the original electrical signal X they output is: X=[10,15,20,25] According to the expression Y=cX+d, the corresponding adjusted signal Y is calculated as: Y=[0.810-2,0.815-2,0.820-2,0.8*25-2]=[6,10,14,18]; Then, a signal strength distribution map D is constructed, which shows the Y values ​​corresponding to different positions. In this simple example, the Y value of each position can be simply listed, but in actual applications, this is a more complex visualization tool or data structure to intuitively display the signal strength of each position.

[0027] Next, calculate the relative stress index Z for each deployment point using the formula Z = sum(Yi^2) / m. For all sensing elements, first calculate the sum of the squares of their respective Y values: sum(Yi^2)=6^2+10^2+14^2+18^2=36+100+196+324=656; Therefore, the relative stress index Z is: Z = sum (Yi^2) / m = 656 / 4 = 164; Through the above process, the adjusted signal Y can be obtained, which reflects the original electrical signal X after linear transformation. This transformation can better adapt to the subsequent data processing needs or compensate for the characteristics of the sensing element. Constructing the signal intensity distribution map D helps to intuitively understand the stress conditions at the locations of each sensing element. The relative stress index Z provides a quantitative way to compare the stress levels between different locations, which is particularly useful for identifying abnormal stress areas.

[0028] For example, in this example, if the Z value is abnormally high, it means there is an overstress problem that needs further investigation. This approach can help engineers quickly locate the problem and take appropriate measures to maintain the health of the integrated circuit chip.

[0029] The third step is to convert the generated initial signal into digital information, and complete the transformation from analog signal to discrete numerical representation through the analog-to-digital converter, which includes: Receive the adjusted signal Y and perform sampling to generate samples Us at discrete time points. The sampling interval is denoted by s. Apply the quantization process to form the value Vs. The formula Vs=round(Us / q)*q, q is the quantization step size; encode the value Vs and construct the binary sequence Ws to ensure that Ws=binary(Vs).

[0030] Embodiment 3

[0031] Assumptions: Sampling interval s = 0.1 seconds (used to define the sampling frequency at discrete time points); Quantization step size q=1 (used for resolution in the quantization process); Assume that the adjusted signal Y changes over time as follows (simplified to discrete values): Y=[6,10,14,18]; According to the expression Vs=round(Us / q)*q, each Y value needs to be sampled and quantized. Since we are dealing directly with Y values ​​here, we can assume that Us=Y. For each Y value, the quantization process is applied to form the value Vs. Since the quantization step size q is set to 1, this means that each Y value is actually rounded to the nearest integer. In this example, all Y values ​​are already integers, so quantization will not change them: Vs=[round(6 / 1)*1,round(10 / 1)*1,round(14 / 1)*1,round(18 / 1)*1]=[6,10,14,18]; Next, encode the value Vs to construct the binary sequence Ws. This means converting each value of Vs into its corresponding binary representation. For example, in an 8-bit system: Ws=[binary(6),binary(10),binary(14),binary(18)]=["00000110","00001010","00001110","00010010"]; Through the above process, the continuous analog signal can be converted into discrete digital information, which is very important for subsequent digital signal processing. The sampling action ensures that snapshots of the signal can be obtained at fixed intervals, while the quantization process simplifies the signal representation so that it can be efficiently processed by the computer. Constructing the binary sequence Ws enables the signal to be stored, transmitted and processed in digital form, greatly improving the speed and efficiency of data processing and reducing the impact of noise.

[0032] In addition, this digital approach allows the use of various advanced algorithms for data analysis, such as pattern recognition, predictive modeling, etc., thereby enhancing the performance and reliability of the stress monitoring system. Ultimately, this approach helps to more accurately monitor the working status of integrated circuit chips, detect potential problems in a timely manner, and ensure the stability and life of the chips.

[0033] The fourth step is to construct a data set representing the relationship between time and stress change based on discrete values; specifically, it includes: Collect the binary sequence Ws and convert Ws back to the quantized stress value Vs using the inverse encoding process Vs=decode(Ws); combine with the sampling time ts to form a time-stress pair TS, using the expression TS=(ts,Vs); summarize all time-stress pairs TS, establish a record set Rs, and arrange them in chronological order, ensuring that Rs={TS1,TS2,...,TSn}.

[0034] Embodiment 4

[0035] Assumptions: The sampling times ts are [0.1, 0.2, 0.3, 0.4] seconds (used for the timestamps in the time-stress pair TS); Assume the following binary sequence Ws (8-bit representation): Ws=["00000110","00001010","00001110","00010010"]; According to the expression Vs=decode(Ws), each Ws value needs to be converted back to a quantized stress value Vs. Here, simply convert the binary number to a decimal integer: Vs=[decode("00000110"),decode("00001010"),decode("00001110"),decode("00010010")]=[6,10,14,18]; Next, combined with the sampling time ts, we form the time-stress pair TS, the expression TS=(ts,Vs). For the example, this would be: TS=[(0.1,6),(0.2,10),(0.3,14),(0.4,18)]; Finally, summarize all time-stress pairs TS to create a record set Rs, arranged in chronological order. In this example, since we already have data points arranged in chronological order, we can create Rs directly: Rs={TS1,TS2,TS3,TS4}={(0.1,6),(0.2,10),(0.3,14),(0.4,18)}; Through the above process, a data set Rs representing the relationship between time and stress is constructed. This data set stores the information of each sampling point in the form of time-stress pairs, making it possible to track the changes of stress over time. This time series data is very suitable for analyzing stress trends, discovering abnormal patterns, and predicting future stress behavior.

[0036] In addition, it also provides a basis for subsequent steps, such as generating mapping charts that intuitively display the dynamics of stress distribution, or performing more complex signal processing and data analysis. Ultimately, this method helps to achieve real-time monitoring of the stress status of integrated circuit chips, ensure their stable operation, and promptly warn of problems that affect performance, thereby ensuring the reliability and safety of the chip throughout its life cycle.

[0037] The fifth step is to use the formed data set to generate a mapping chart that intuitively displays the dynamics of stress distribution, including: Read the data record set Rs, calculate the stress change amplitude ΔL of each deployment point at different time points, and use ΔL=Lt-L(t-1), where Lt represents the stress value at the current time point t, and L(t-1) represents the stress value at the previous time point t-1; Create a color coding rule Cc to map stress changes to a visual color range, the expression Cc=colormap(ΔL); Draw a mapping chart M, integrate the color information of all deployment points, and ensure that M=plot(T,P,Cc), where T is the time axis and P is the location coordinate of the deployment point.

[0038] Embodiment 5

[0039] Assumptions: The deployment point location coordinates P are [P1, P2, P3, P4] (used for mapping location information in the chart); The time axis T is [0.1, 0.2, 0.3, 0.4] seconds (used to map the time axis of the chart); Suppose there is the following set of records Rs arranged in chronological order: Rs={(0.1,6),(0.2,10),(0.3,14),(0.4,18)}; First, you need to calculate the stress change amplitude ΔL of each deployment point at different time points, using the expression ΔL=Lt-L(t-1). Since the example only has data from one sensing element, this data will be used to simulate the change amplitude of one deployment point. For multiple deployment points, you need to repeat this process for each point: Assume that a new stress value is obtained at the next time point: Rs_new={(0.5,22)}; Then, the change amplitude ΔL between the time points 0.4 seconds and 0.5 seconds will be: ΔL=Lt-L(t-1)=22-18=4; If there is more historical data, you can calculate the ΔL for each time interval and create a list to store them. For example, if all time intervals are the same, you will get the following ΔL list (here is just an example): ΔL_list=[4,4,4,4]; Next, create a color coding rule Cc to map the stress changes to a visual color range. This usually involves choosing an appropriate color mapping function colormap() that can assign different colors based on the ΔL value. For example, the color can be set to change from blue to red when ΔL increases. The specific implementation depends on the plotting library or tool used, but it can be simplified as: Cc=colormap(ΔL); In this example, we can simply assign some colors to correspond to ΔL values, such as: When ΔL<=0: blue; When 0<ΔL<=2: green; When 2<ΔL<=4: yellow; When ΔL>4: red; Finally, a mapping chart M is drawn to integrate the color information of all deployment points. Here, T is the time axis, P is the location coordinate of the deployment point, and Cc is the color coding based on the stress change. Because the example only has a single deployment point, the location coordinate P can be omitted or fixed. The actual mapping chart will combine the time axis T and the stress change amplitude ΔL to display the color information. If you want to draw a chart containing multiple deployment points, you will traverse all Ts and corresponding Ps and apply the correct color according to Cc.

[0040] Through the above process, a mapping chart M is generated to intuitively show the dynamics of stress distribution. The chart uses time and space as axes and uses color coding to show the change of stress over time. This visualization method can help engineers quickly understand the development trend of stress and identify the location and time point of abnormal stress growth. It can also be used to compare stress changes between different deployment points and find potential problem areas.

[0041] In addition, this method facilitates deeper data analysis, such as pattern recognition, trend prediction, etc., to optimize the design and operating conditions of integrated circuit chips. Ultimately, using mapping charts, engineers can more effectively monitor and maintain the health of chips to ensure their stability and reliability.

[0042] The sixth step is to parse the mapping chart to determine where the abnormal pattern appears in the chart; specifically, it includes: Analyze the color distribution in the mapping chart M, calculate the color deviation degree Dp of each deployment point P. The formula is Dp(P)=|Cc(P)-C_avg|, where Cc(P) is the color coding value of deployment point P, C_avg is the average value of the color coding values of all deployment points, and Dp(P) represents the color deviation degree of deployment point P; establish a threshold Th to distinguish normal and abnormal color changes. The expression is Th=median(Dp)+k*IQR(Dp), where median(Dp) represents the median of the color deviation degrees Dp(P) of all deployment points, IQR(Dp) represents the interquartile range of the color deviation degrees Dp(P) of all deployment points, and k is a coefficient used to adjust the sensitivity of the threshold Th; screen out the deployment points P that exceed the range of Th to form a suspected abnormal set Ep, with the condition Ep={P|Dp(P)>Th}; perform a spatial correlation assessment on the members within Ep. If F(P1,P2)=dist(P1,P2)<d_max and P1,P2∈Ep are satisfied, it is marked as an abnormal pattern area Ra. <d_max is the maximum distance threshold used to define the maximum allowable distance between two deployment points.

[0043] Example Six

[0044] Hypothetical conditions: The location coordinates P of the deployment points are [P1, P2, P3, P4] (for the location information in the mapping chart); The color coding values Cc(P) of all deployment points are [0.2, 0.5, 0.7, 0.9] (representing different stress levels, expressed as normalized values); The maximum distance d_max for spatial correlation assessment = 0.5 (the unit depends on the actual application); The coefficient k = 1.5 (the coefficient used to determine the threshold Th); First, calculate the average value C_avg of the color coding values of all deployment points: C_avg = sum(Cc(P)) / n = (0.2 + 0.5 + 0.7 + 0.9) / 4 = 2.3 / 4 = 0.575; Then, analyze the color distribution in the mapping chart M and calculate the color deviation degree Dp of each deployment point P: Dp(P1)=|Cc(P1)-C_avg| = |0.2 - 0.575| = 0.375 Dp(P2)=|Cc(P2)-C_avg| = |0.5 - 0.575| = 0.075 Dp(P3)=|Cc(P3)-C_avg| = |0.7 - 0.575| = 0.125 Dp(P4)=|Cc(P4)-C_avg| = |0.9 - 0.575| = 0.325; Next, establish a threshold Th to distinguish normal from abnormal color changes. Here, it is necessary to calculate the median of Dp and the interquartile range IQR(Dp): The sorted Dp list is [0.075, 0.125, 0.325, 0.375]. The median median(Dp) = (0.125 + 0.325) / 2 = 0.225. The interquartile range IQR(Dp) = the third quartile - the first quartile = 0.325 - 0.075 = 0.25; Therefore, the threshold Th is: Th = median(Dp) + k * IQR(Dp) = 0.225 + 1.5 * 0.25 = 0.225 + 0.375 = 0.6; Filter out the deployment points P that exceed the Th range to form a suspected abnormal set Ep: Ep = {P|Dp(P) > Th} = {P1, P4} (because Dp(P1) = 0.375 and Dp(P4) = 0.325 are both greater than Th = 0.6); Finally, perform a spatial correlation assessment on the members within Ep. Assume the defined spatial coordinates are: the coordinates of P1 are (x1, y1), and the coordinates of P4 are (x4, y4); If F(P1, P4) = dist(P1, P4) < d_max is satisfied, it is marked as an abnormal pattern area Ra. For this example, assume dist(P1, P4) = 0.3 < d_max, then: Ra = {P1, P4}; Through the above process, abnormal patterns appearing in the mapping chart can be identified and located. Specifically, by analyzing the color distribution, calculating the color deviation degree, and setting a reasonable threshold, the deployment points that show significant stress changes different from the average level can be effectively found. Further spatial correlation assessment helps to confirm whether these abnormal points form a coherent abnormal pattern area Ra, which is where stress problems break out intensively.

[0045] This method can help engineers quickly lock in the chip areas that require special attention, take timely measures for investigation or maintenance, and thus prevent potential problems from causing more serious failures. Ultimately, this method improves the accuracy and response speed of the stress monitoring system, ensuring the safe operation of integrated circuit chips.

[0046] Step 7, based on the identified positions of abnormal patterns, delimit the scope of the problematic area, specifically including: Get the marked abnormal pattern area Ra; for each abnormal position Ra, determine its neighborhood Na, which is defined as the set of all other deployment points whose distance from Ra does not exceed r, and the expression Na={Q|dist(Ra,Q)≤r}, where r is the distance threshold, indicating the maximum radius of the neighborhood; calculate the color coding average value Avg_Cc of all deployment points in each neighborhood Na, using Avg_Cc(Na)=sum(Cc(Q)) / count(Q), where Cc(Q) represents the color coding value of the deployment point Q, and count(Q) is a counting operation, indicating the number of deployment points in the neighborhood Na; compare the neighborhood average color coding Avg_Cc with the overall average color coding C_avg, and when the difference ΔCc=|Avg_Cc-C_avg| exceeds the set threshold Th, mark the neighborhood Na as a potential problem area Pa, and the conditional expression is Pa={Na|ΔCc>Th}.

[0047] Embodiment 7

[0048] Assumptions: The abnormal pattern region Ra contains [P1, P4] (based on the results of the previous step); Neighborhood radius r = 0.6 (the unit depends on the actual application); Set the threshold Th = 0.3 (used to determine whether the neighborhood is marked as a potential problem area); First, obtain the marked abnormal pattern area Ra: Ra={P1,P4}; For each abnormal location Ra, determine its neighborhood Na. Assume that the location coordinates of all deployment points are expressed in the following (x, y) format: P1:(0.1,0.2); P2:(0.5,0.4); P3:(0.8,0.9); P4:(0.7,0.6); Calculate the neighborhood Na of P1 and P4 respectively. Here we assume that the dist() function returns the Euclidean distance between two points and all the deployed points are within the range of consideration. If the distance is less than or equal to r, the point belongs to the neighborhood Na.

[0049] For P1: dist(P1,P2)=sqrt((0.5-0.1)^2+(0.4-0.2)^2)=sqrt(0.16+0.04)≈0.447 <r; dist(P1,P3)>r; dist(P1,P4)>r; Therefore, Na(P1)={P2}; For P4: dist(P4,P2) <r; dist(P4,P3) <r; dist(P4,P1)>r; Therefore, Na(P4)={P2,P3}; Next, calculate the color coding average value Avg_Cc of all deployment points in each neighborhood Na. Assume that the color coding Cc is: Cc(P1)=0.2; Cc(P2)=0.5; Cc(P3)=0.7; Cc(P4)=0.9; So: Avg_Cc(Na(P1))=sum(Cc(Q)) / count(Q)=0.5 / 1=0.5Avg_Cc(Na(P4))=(0.5+0.7) / 2=0.6; Then, compare the neighborhood average color code Avg_Cc with the overall average color code C_avg. Assume that the overall average color code C_avg is 0.575 calculated in the previous step.

[0050] The difference ΔCc is: ΔCc(P1)=|Avg_Cc(Na(P1))-C_avg|=|0.5-0.575|=0.075ΔCc(P4)=|Avg_Cc(Na(P4))-C_avg|=|0.6-0.575|=0.025; When the difference ΔCc exceeds the set threshold Th, the neighborhood Na is marked as a potential problem area Pa. Since the ΔCc values ​​of are all less than Th=0.3, in this case, no neighborhood will be marked as a potential problem area Pa. However, if the threshold is adjusted or there are other circumstances that make ΔCc exceed Th, the corresponding neighborhood will be marked.

[0051] Through the above process, the problematic area can be delineated based on the location of the identified abnormal pattern. This method helps to more accurately locate the extension of stress anomalies, rather than just the anomalies of a single point. By analyzing the color-coded averages within the neighborhood and comparing them with the overall average, areas that adversely affect chip performance can be found.

[0052] This approach allows engineers to take preventive maintenance measures in advance, optimize the design and operating conditions of integrated circuits, and ensure the stability and reliability of the system. Ultimately, this approach improves the accuracy of fault diagnosis and helps avoid potential problems from escalating, thus ensuring the healthy operation of the chip throughout its life cycle.

[0053] Step 8: Initiate targeted testing procedures to confirm the actual existence of abnormal stress conditions in the identified problem areas; specifically: Select the marked potential problem area Pa; For each target area Pa, deploy a sensing element Hs and record the newly generated signal strength S_new to ensure that S_new = measure(Hs); Compare the new signal strength S_new with the original signal strength S_original, and evaluate the stress change by Ratio=S_new / S_original; If Ratio>Limit, it is confirmed that there is an actual abnormal stress condition in the area, and this area is included in the final abnormal list La. The conditional expression is La={Pa|Ratio>Limit}, where Limit is a preset threshold used to determine whether Ratio represents a significant stress change.

[0054] Embodiment 8

[0055] Assumptions: The potential problem area Pa contains [Na(P1), Na(P4)] (based on the results of the previous step); Signal strength ratio evaluation threshold Limit=1.2 (used to confirm the actual abnormal stress condition); First, select the marked potential problem area Pa: Pa = {Na(P1), Na(P4)}; For each target area Pa, a sensing element Hs is deployed and the newly generated signal strength S_new is recorded. Assume that the following new measurement results are obtained: For the new signal strength of Na(P1), S_new(Na(P1))=measure(Hs); For the new signal strength of Na(P4), S_new(Na(P4))=measure(Hs); Assume that the original signal strength S_original is: S_original(Na(P1))=5; S_original(Na(P4))=7; And the new measured signal strength is as follows (the unit depends on the specific application): S_new(Na(P1))=6; S_new(Na(P4))=8; Next, compare the new signal strength S_new with the original signal strength S_original, and evaluate the stress change by Ratio=S_new / S_original: Ratio(Na(P1))=S_new(Na(P1)) / S_original(Na(P1))=6 / 5=1.2; Ratio(Na(P4))=S_new(Na(P4)) / S_original(Na(P4))=8 / 7≈1.143; Finally, if Ratio>Limit, it is confirmed that there is an actual abnormal stress condition in the area, and this area is included in the final abnormal list La. According to the setting, only when Ratio is greater than Limit will it be added to La. Therefore: For Na(P1), since Ratio(Na(P1))=1.2 is equal to Limit, it depends on how to interpret the equal situation; if it is strictly greater, then it will not be added.

[0056] For Na(P4), since Ratio(Na(P4))≈1.143 is less than Limit, it will not be added.

[0057] If we slightly adjust the data so that Ratio(Na(P1)) is indeed greater than Limit, for example, Ratio(Na(P1))=1.25, then the final anomaly list La will be: La={Pa|Ratio>Limit}={Na(P1)}; Through the above process, targeted testing procedures can be initiated for the delineated problem areas to confirm whether there are actual abnormal stress conditions. This approach ensures secondary verification of suspected problematic areas, thereby reducing the risk of false positives. By accurately comparing the strength of new and old signals and calculating their ratio, it is possible to effectively assess whether stress changes are beyond the normal range. Ultimately, this approach helps to more accurately determine which areas do have actual abnormal stress conditions that require attention and include them in the final abnormality list La so that further measures can be taken, such as failure analysis, design improvements, or preventive maintenance.

[0058] Step 9: After the test, an alarm message is output to the monitoring system to indicate the specific location of the abnormal stress area; specifically including: Collect the confirmed final anomaly list La; For each abnormal area Pa, an alarm data packet Ba is constructed, including the position coordinates P, stress change ratio Ratio and timestamp Ts, and the expression Ba=(P, Ratio, Ts); Convert Ba into the format Fa that can be parsed by the monitoring system, using Fa=encode(Ba); Send the converted alarm information Fa, and record the sending status Sa at the same time. Use ACK to verify the successful transmission. The conditional expression is Sa=send(Fa) and check(ACK). If ACK==True, it means that the alarm has been successfully transmitted.

[0059] Embodiment 9

[0060] Assumptions: The final anomaly list La contains [Na(P1)] (based on the results of the previous step); Timestamp Ts="2025-01-0618:30:00" (time information for alarm data packets); First, collect the confirmed final anomaly list La: La = {Na(P1)}; For each abnormal area Pa, an alarm data packet Ba is constructed, including the position coordinates P, stress change ratio Ratio and timestamp Ts. Assume the following information: The position coordinates of the abnormal area Na(P1) are P1=(0.1,0.2); Stress change ratio Ratio(Na(P1))=1.25; Timestamp Ts = "2025-01-0618:30:00"; Then, the constructed alarm data packet Ba is: Ba(Na(P1))=(P1,Ratio(Na(P1)),Ts)=((0.1,0.2),1.25,"2025-01-0618:30:00"); Next, convert Ba into a monitoring system parseable format Fa. Here we assume that the encode() function can convert the alarm data packet into a data format suitable for transmission, such as JSON or XML. Taking JSON as an example, the encoded Fa is as follows: json Fa={"location":{"x":0.1,"y":0.2},"ratio":1.25,"timestamp":"2025-01-0618:30:00"}; Then, send the converted alert message Fa and record the sending status Sa. Assume that the send() function is responsible for sending the alert message and returns an ACK signal to verify the successful transmission. If ACK==True, it means that the alert has been successfully communicated. In this example, it can be assumed that the sending process is successful: plaintext Sa=send(Fa)check(ACK)#Assume this step check returns True; If ACK==True, it means that the alarm information has been successfully transmitted to the monitoring system.

[0061] Through the above process, alarm information can be effectively output to the monitoring system, indicating the specific location of abnormal stress areas. This method ensures that the monitored abnormal conditions can be communicated to the relevant systems or personnel in a timely manner so that quick action can be taken. By constructing a detailed alarm data packet Ba, including location coordinates, stress change ratio and timestamp, sufficient information is provided to understand the nature of the problem and the time and place of occurrence. Converting the alarm data packet into a format Fa that can be parsed by the monitoring system ensures that information can be seamlessly transmitted between different systems.

[0062] Finally, the successful transmission of the alarm information is verified through the ACK mechanism, which improves the reliability of communication and ensures that critical alarms are not lost. This approach not only improves the speed of fault response, but also enhances the overall safety of the system, helps maintain the stable operation of the integrated circuit chip, and provides valuable data support for subsequent analysis and preventive measures.

[0063] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A stress detection method for an integrated circuit chip, characterized in that: include: Deploy a sensing element at a preset position inside the integrated circuit to collect stress data, and generate an initial signal based on the data obtained by the sensing element; The generated initial signal is converted into digital information, and the conversion from analog signal to discrete numerical representation is completed through an analog-to-digital converter. According to the discrete numerical values, a data set representing the relationship between time and stress change is constructed; Using the formed data set, a mapping chart is generated to intuitively display the dynamics of stress distribution, and the mapping chart is analyzed to determine the location of abnormal patterns in the chart; Based on the identified abnormal pattern location, the problematic area is delineated, and targeted testing procedures are initiated for the delineated problematic area to confirm the actual existence of abnormal stress conditions; After the test, an alarm message is output to the monitoring system, indicating the specific location of the abnormal stress area.

2. The integrated circuit chip stress detection method according to claim 1, characterized in that: The step of disposing a sensing element at a preset position inside the integrated circuit to collect stress data includes: When the sensing element is activated, an initial electrical signal U is generated. The initial electrical signal U is proportional to the stress level L. The expression is U=k*L, where k is a proportional factor, which is used to express the proportional relationship between the initial electrical signal U and the stress level L. Apply a linear transformation to the original signal U to generate a standardized signal G, the formula is G=a*U+b, a is a scaling factor used to adjust the amplitude of the original signal U, b is an offset used to numerically shift the standardized signal G; Based on the standardized signal G, the average stress level M is calculated using M=sum(Gi) / n, where n represents the number of samples of the standardized signal G used to calculate the average stress level M, and Gi represents the i-th standardized signal value; When M is greater than the preset threshold Th, the subsequent response action is triggered, and the condition is M>Th.

3. The integrated circuit chip stress detection method according to claim 2, characterized in that: The generating of the initial signal according to the data acquired by the sensing element comprises: Reading the original electrical signal X output by the sensing element, the strength of the original electrical signal X is associated with the local stress condition; Apply a linear transformation to the original electrical signal X to generate an adjusted signal Y, calculated by Y=c*X+d, where c is a scaling factor used to adjust the amplitude of the original electrical signal X, and d is an offset used to numerically shift the adjusted signal Y; Construct a signal strength distribution map D, where the signal strength distribution map D displays Y values ​​corresponding to different positions; Calculate the relative stress index Z of each deployment point using Z=sum(Yi^2) / m, where Yi represents the adjusted signal value output by the i-th sensing element at a certain moment, and m represents the number of adjusted signal values ​​involved in calculating the relative stress index Z.

4. The integrated circuit chip stress detection method according to claim 3, characterized in that: The step of converting the generated initial signal into digital information comprises: Receive the adjusted signal Y and perform sampling to generate samples Us at discrete time points. The sampling interval is denoted by s. Apply the quantization process to form a value Vs. The formula Vs=round(Us / q)*q, q is the quantization step size. Encode the value Vs and construct a binary sequence Ws, ensuring that Ws=binary(Vs).

5. The integrated circuit chip stress detection method according to claim 4, characterized in that: The step of constructing a data set representing the relationship between time and stress change according to discrete values ​​includes: Collect the binary sequence Ws, convert Ws back to the quantized stress value Vs using the inverse encoding process Vs = decode(Ws); Combine with the sampling time ts to form the time-stress pair TS, with the expression TS = (ts, Vs); Summarize all time-stress pairs TS to establish a record set Rs, arranged in chronological order, ensuring Rs = {TS1, TS2,..., TSn}.

6. The integrated circuit chip stress detection method according to claim 5, characterized in that: Using the formed data set, generate a mapping chart that intuitively shows the dynamic stress distribution, including: Read the data record set Rs, calculate the stress change amplitude ΔL at each deployment point at different time points, using ΔL = Lt - L(t - 1), where Lt represents the stress value at the current time point t and L(t - 1) represents the stress value at the previous time point t - 1; Create a color coding rule Cc to map the stress change to the visual color range, with the expression Cc = colormap(ΔL); Plot the mapping chart M, integrating the color information of all deployment points, ensuring M = plot(T, P, Cc), where T is the time axis and P is the position coordinates of the deployment point.

7. The integrated circuit chip stress detection method according to claim 6, characterized in that: Parsing the mapping chart to determine the positions where abnormal patterns appear in the chart, including: Analyze the color distribution in the mapping chart M, calculate the color deviation degree Dp of each deployment point P, with the formula Dp(P) = |Cc(P) - C_avg|, where Cc(P) is the color coding value of the deployment point P and C_avg is the average of the color coding values of all deployment points, and Dp(P) represents the color deviation degree of the deployment point P; Establish a threshold Th to distinguish normal and abnormal color changes, with the expression Th = median(Dp) + k * IQR(Dp), where median(Dp) represents the median of the color deviation degrees Dp(P) of all deployment points, IQR(Dp) represents the interquartile range of the color deviation degrees Dp(P) of all deployment points, and k is a coefficient used to adjust the sensitivity of the threshold Th; Screen out the deployment points P that exceed the Th range to form a suspected abnormal set Ep, with the condition Ep = {P|Dp(P) > Th}; Perform a spatial correlation assessment on the members within Ep. If it satisfies F(P1, P2) = dist(P1, P2) < d_max and P1, P2 ∈ Ep, then mark it as an abnormal pattern area Ra. <d_max is the maximum distance threshold used to define the maximum allowable distance between two deployment points.

8. The integrated circuit chip stress detection method according to claim 7, characterized in that: Based on the identified positions of abnormal patterns, delimit the scope of the problematic area, including: Obtain the marked abnormal pattern area Ra; For each abnormal position Ra, determine its neighborhood Na, defined as the set consisting of all other deployment points whose distance from Ra does not exceed r, with the expression Na = {Q|dist(Ra, Q) ≤ r}, where r is the distance threshold representing the maximum radius of the neighborhood; Calculate the color coding average value Avg_Cc of all deployment points in each neighborhood Na, using Avg_Cc(Na)=sum(Cc(Q)) / count(Q), where Cc(Q) represents the color coding value of the deployment point Q, and count(Q) is a counting operation, indicating the number of deployment points in the neighborhood Na; Compare the neighborhood average color code Avg_Cc with the overall average color code C_avg. When the difference ΔCc=|Avg_Cc-C_avg| exceeds the set threshold Th, the neighborhood Na is marked as a potential problem area Pa, and the conditional expression is Pa={Na|ΔCc>Th}.

9. The integrated circuit chip stress detection method according to claim 8, characterized in that: For the identified problem areas, targeted testing procedures are initiated to confirm the actual existence of abnormal stress conditions, including: Select the marked potential problem area Pa; For each target area Pa, deploy a sensing element Hs and record the newly generated signal strength S_new to ensure that S_new = measure(Hs); Compare the new signal strength S_new with the original signal strength S_original, and evaluate the stress change by Ratio=S_new / S_original; If Ratio>Limit, it is confirmed that there is an actual abnormal stress condition in the area, and this area is included in the final abnormal list La. The conditional expression is La={Pa|Ratio>Limit}, where Limit is a preset threshold used to determine whether Ratio represents a significant stress change.

10. The integrated circuit chip stress detection method according to claim 9, characterized in that: After the test, an alarm message is output to the monitoring system, indicating the specific location of the abnormal stress area, including: Collect the confirmed final exception list La; For each abnormal area Pa, an alarm data packet Ba is constructed, including the position coordinates P, stress change ratio Ratio and timestamp Ts, and the expression Ba=(P, Ratio, Ts); Convert Ba into the format Fa that can be parsed by the monitoring system, using Fa=encode(Ba); Send the converted alarm information Fa, and record the sending status Sa at the same time. Use ACK to verify the successful transmission. The conditional expression is Sa=send(Fa) and check(ACK). If ACK==True, it means that the alarm has been successfully transmitted.

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