Performance test method and system based on ultra-thin touch switch color film

Abstract

The present application relates to the technical field of performance test, and specifically includes a performance test method and system based on an ultra-thin touch switch color film, which comprises: configuring an electric performance test parameter to connect a signal generator and an oscilloscope, introducing touch and force data, analyzing parasitic capacitance disturbance signal and time-frequency correlation, determining interference strength and time delay, correlating physical defect type mapping failure path to generate a reminder signal. The technical problem that the single-point test method has limited analysis capability for parasitic capacitance disturbance signal and cannot accurately grasp the signal interference characteristics is solved, the electric performance test parameter adaptive configuration driven by material parameters is realized, the test parameters can be automatically adjusted according to different color film material characteristics, the test accuracy and effectiveness are improved, meanwhile, the traditional single-point test limitation is broken through, the signal interference characteristics are accurately quantified, the signal interference situation is deeply understood, combined with failure mechanism analysis, the fault root cause is quickly located, and the fault occurrence probability is reduced.

Description

Technical Field

[0001] This invention relates to the field of performance testing technology, specifically to a performance testing method and system based on an ultra-thin touch switch color filter. Background Technology

[0002] With the widespread application of ultra-thin touch switch color filters in electronic devices, their performance testing is crucial for ensuring product quality. Performance testing of ultra-thin touch switch color filters often adopts a method of pre-setting fixed test parameters and combining single-point testing. The test is completed by detecting simple electrical performance indicators, which is difficult to meet diverse testing needs.

[0003] Fixed electrical performance test parameters cannot be adaptively adjusted according to the characteristics of the color filter material, making it difficult to achieve optimal test schemes; single-point testing methods can only obtain a small amount of performance data, making it impossible to comprehensively analyze parasitic capacitance disturbance signals and accurately quantify signal interference characteristics; there is a lack of in-depth research on the correlation between signal interference, physical defects, and failure paths, making it impossible to move from phenomenon detection to root cause localization; single test parameters cannot effectively simulate the changes in touch switch color filter performance with touch and force in real-world usage scenarios; test results are mostly data listings, lacking effective analysis and transformation, and unable to provide an actionable basis for maintenance decisions.

[0004] In summary, existing technologies suffer from limitations in the ability of single-point testing methods to analyze parasitic capacitance disturbance signals, making it impossible to accurately grasp the characteristics of signal interference. Summary of the Invention

[0005] This application provides a performance testing system based on an ultra-thin touch switch color filter, aiming to solve the technical problem that existing single-point testing methods have limited ability to analyze parasitic capacitance disturbance signals and cannot accurately grasp the signal interference characteristics.

[0006] In view of the above problems, the technical solution to achieve the present application is as follows:

[0007] This application provides a performance testing method based on an ultra-thin touch switch color filter, wherein the method includes:

[0008] An ultra-thin touch switch color filter is placed on a test platform, and basic material parameters are uploaded to the test platform. The test platform is configured with electrical performance test parameters based on the basic material parameters. The test platform is connected to a signal generator and an oscilloscope. After initializing the test platform with the electrical performance test parameters, touch signals and contact force data are introduced. Parasitic capacitance disturbance signals are recorded during the electrical performance test. The frequency components of the parasitic capacitance disturbance signals are decomposed, disturbance feature vectors are extracted, and time-frequency domain synchronous correlation analysis is performed with the touch signals to determine the signal interference intensity and signal interference delay of the parasitic capacitance disturbance signals on the touch signals. Based on the signal interference intensity and signal interference delay, multi-point correlation mapping is performed in conjunction with physical defect types. Then, critical failure paths are determined through failure mechanism analysis. The critical failure paths are written into a failure alert signal.

[0009] In another aspect, this application provides a performance testing system based on an ultra-thin touch switch color filter, wherein the system includes:

[0010] The performance testing module is used to place the ultra-thin touch switch color film on the testing platform and upload basic material parameters to the testing platform; the testing platform is configured with electrical performance testing parameters based on the basic material parameters; the initialization configuration module is used to connect the testing platform with a signal generator and an oscilloscope; after initializing the testing platform with the electrical performance testing parameters, touch signals and contact force data are introduced; the frequency decomposition module is used to record parasitic capacitance disturbance signals during the electrical performance testing process, decompose the frequency components of the parasitic capacitance disturbance signals, extract the disturbance feature vector, and perform time-frequency domain synchronous correlation analysis with the touch signals to determine the signal interference intensity and signal interference delay of the parasitic capacitance disturbance signals on the touch signals; the failure reminder module is used to perform multi-point correlation mapping based on the signal interference intensity and signal interference delay, combined with the physical defect type, and then determine the critical failure path through failure mechanism analysis; the critical failure path is written into the failure reminder signal.

[0011] In summary, one or more technical solutions provided in this application achieve adaptive configuration of electrical performance test parameters driven by material parameters. This allows for automatic adjustment of test parameters based on the characteristics of different color filter materials, improving test accuracy and effectiveness. At the same time, it breaks through the limitations of traditional single-point testing, accurately quantifies signal interference characteristics, provides in-depth understanding of signal interference, and, combined with failure mechanism analysis, quickly locates the root cause of the fault, reducing the probability of fault occurrence. Attached Figure Description

[0012] Figure 1 A flowchart illustrating the performance testing method based on an ultra-thin touch switch color filter is provided for this application;

[0013] Figure 2 This application provides a structural schematic diagram of a performance testing system based on an ultra-thin touch switch color filter.

[0014] Explanation of reference numerals in the attached diagram: Performance testing module M100, initialization configuration module M200, frequency decomposition module M300, and failure alert module M400. Detailed Implementation

[0015] Example 1

[0016] The present application will now be described in detail with reference to the accompanying drawings, such as... Figure 1 As shown, this application provides a performance testing method based on an ultra-thin touch switch color filter, wherein the method includes:

[0017] S1: Place the ultra-thin touch switch color film on the test platform and upload the basic material parameters to the test platform; configure the electrical performance test parameters on the test platform using the basic material parameters; S2: Connect the test platform to a signal generator and an oscilloscope; after initializing the test platform with the electrical performance test parameters, introduce touch signals and contact force data.

[0018] Specifically, ultra-thin touch switch color filters refer to touch-sensitive films used in touch devices that are thin and lightweight and integrate color display functions. Their performance directly affects the touch sensitivity and display effect of the device. The testing platform is an integrated hardware and software system used to perform performance testing on ultra-thin touch switch color filters. Basic material parameters include data reflecting the physicochemical properties of the material, such as the material type, thickness, and dielectric constant of the color filter. Electrical performance test parameters are indicators set based on the basic material parameters to measure the electrical performance of the color filter, such as voltage, current, and frequency test standards. A signal generator is used to generate electrical signals simulating touch operations, and an oscilloscope can visually display the waveform of the electrical signals, facilitating observation and analysis of signal characteristics.

[0019] First, the ultra-thin touch switch color filter is placed and fixed on the test platform to ensure a stable testing environment. Then, the basic material parameters of the color filter, such as a thickness of 0.1mm and a dielectric constant of 3, are uploaded to the test platform. Based on these parameters, the test platform calculates and configures appropriate electrical performance test parameters using its built-in algorithm, such as setting the test voltage to 5V and the test frequency to 100Hz. Next, the test platform is connected to a signal generator and an oscilloscope. The platform is initialized using the configured electrical performance test parameters. Then, simulated touch signals are introduced through the signal generator, and different contact force data, such as 1N and 2N, are set to simulate the pressing situation in actual use. Uploading the basic material parameters and configuring the electrical performance test parameters appropriately ensures that subsequent tests are based on the characteristics of the color filter itself, making the test results more targeted and reliable. Connecting the signal generator and oscilloscope and introducing touch signals and contact force data simulates real-world usage scenarios, providing an effective data foundation for accurate analysis of the color filter's performance.

[0020] S3: Record the parasitic capacitance disturbance signal during the electrical performance test, decompose the frequency components of the parasitic capacitance disturbance signal, extract the disturbance feature vector, and perform time-frequency domain synchronous correlation analysis with the touch signal to determine the signal interference intensity and signal interference delay of the parasitic capacitance disturbance signal on the touch signal; S4: Based on the signal interference intensity and signal interference delay, perform multi-point correlation mapping in combination with the physical defect type, and then determine the critical failure path through failure mechanism analysis; write the critical failure path into the failure reminder signal.

[0021] Specifically, parasitic capacitance disturbance signals refer to electrical signal fluctuations caused by unexpected capacitance changes due to factors such as circuit layout and material characteristics during the electrical performance testing of ultra-thin touch switch color filters. These fluctuations interfere with normal touch signal transmission. Frequency component decomposition breaks down the complex parasitic capacitance disturbance signal into sinusoidal components of different frequencies for clearer signal characteristic analysis. The disturbance feature vector is a set of values ​​extracted from the decomposed frequency components that characterize the parasitic capacitance disturbance signal, such as the amplitude and phase of each frequency component. Time-frequency domain synchronization correlation analysis simultaneously performs correlation analysis between the parasitic capacitance disturbance signal and the touch signal in both the time and frequency domains to determine their synchronization relationship and degree of mutual influence. Signal interference intensity indicates the extent of interference of the parasitic capacitance disturbance signal on the touch signal, while signal interference delay refers to the time delay of the interference signal relative to the original touch signal. Physical defect types include physical damage or abnormalities such as scratches on the color filter surface, internal short circuits, and material inhomogeneity. Multi-point correlation mapping establishes a multi-dimensional correspondence between signal interference intensity, signal interference delay, and different physical defect types. Failure mechanism analysis studies the impact of material, structural, and environmental factors on product performance to reveal the root causes and mechanisms of product failure. Critical failure paths refer to the most significant physical processes or mechanisms that lead to product performance degradation or failure.

[0022] During electrical performance testing, parasitic capacitance disturbance signals are continuously recorded. The frequency components of these signals are decomposed using methods such as Fourier transform, extracting characteristic values ​​such as amplitude and phase of each frequency component to form a disturbance feature vector. Then, this disturbance feature vector is synchronously correlated with the touch signal in the time-frequency domain. By calculating statistical quantities such as correlation coefficients, the signal interference intensity and signal interference delay of the parasitic capacitance disturbance signal on the touch signal are determined. Next, based on these interference data and pre-defined association rules (such as surface scratches corresponding to interference intensity in the 20%-40% range and interference delay in the 3ms-7ms range), multi-point correlation mapping is performed. Subsequently, through failure mechanism analysis, the influence of material properties, structural characteristics, and testing environment on failure is studied to determine critical failure paths such as surface scratches → increased parasitic capacitance → enhanced signal interference → touch malfunction. Finally, the determined critical failure paths, such as the failure path information in the example above, are written into the failure alert signal.

[0023] By conducting in-depth analysis of parasitic capacitance disturbance signals and studying their correlation with touch signals, we can accurately quantify signal interference characteristics, providing crucial data support for subsequent physical defect localization and failure path analysis. The combination of multi-point correlation mapping and failure mechanism analysis enables in-depth diagnosis from signal interference phenomena to the root causes of physical defects. This allows us to accurately identify the key reasons for product performance degradation, write critical failure paths into failure alert signals, and transform complex test analysis results into intuitive and operable maintenance information, thereby improving product maintenance efficiency and reliability.

[0024] Furthermore, the method of this application includes:

[0025] The basic material parameters include film thickness, electrode material, and touch sensing sensitivity, and an equivalent circuit model of the ultra-thin touch switch color filter is constructed. Based on the equivalent circuit model of the ultra-thin touch switch color filter located on the test platform, the impedance characteristics and capacitive reactance characteristics at different frequencies are obtained. Through the impedance characteristics and capacitive reactance characteristics at different frequencies, the test voltage range, test current threshold, and frequency scanning interval are determined, and the electrical performance test parameters are set.

[0026] Specifically, the equivalent circuit model is a mathematical model that abstracts the physical structure and electrical characteristics of the ultra-thin touch switch color filter into a combination of circuit elements, used to simulate its behavior in electrical performance testing. Impedance characteristics reflect the frequency dependence of the circuit's resistance to current, while capacitive reactance characteristics reflect the capacitor's ability to impede alternating current at different frequencies. The test voltage range is a voltage interval that ensures test safety and accurately reflects the color filter's performance; the test current threshold is the critical current value used to determine whether the circuit is operating normally; and the frequency scan range is the frequency range used for testing.

[0027] Basic material parameters such as film thickness (e.g., 0.1 mm), electrode material (e.g., ITO), and touch sensitivity (e.g., 5 mV / N) are input into specialized software (e.g., COMSOL) to construct an equivalent circuit model including components such as resistors and capacitors. Impedance spectrum testing is performed on the model within a frequency range to obtain impedance and capacitive reactance values ​​at different frequencies. By analyzing the resonant point of the impedance-frequency curve (e.g., a peak appears at 5 kHz) and the trend of capacitive reactance changes, and combining this with the material breakdown voltage, the test voltage range is determined. A current threshold is set according to Ohm's law and a safety factor. Segments sensitive to impedance changes are selected as frequency scanning intervals to provide accurate parameters for subsequent testing. Setting a reasonable voltage range avoids the risk of breakdown, the current threshold ensures test safety, and the frequency scanning interval covers the main operating frequency band.

[0028] Furthermore, the method of this application includes recording parasitic capacitance disturbance signals during the electrical performance testing process, decomposing the frequency components of the parasitic capacitance disturbance signals, and extracting disturbance feature vectors.

[0029] The parasitic capacitance disturbance signal in the time domain is converted into a frequency domain signal using FFT; the frequency domain signal is decomposed into multiple layers to obtain the energy distribution of sub-signals in each frequency band, and the energy proportion, center frequency, and bandwidth of each frequency band are extracted as constituent elements of the disturbance feature vector.

[0030] Specifically, FFT (Fast Fourier Transform) is an efficient algorithm for calculating the Discrete Fourier Transform, which can convert time-domain signals into frequency-domain signals and reveal the frequency components of the signal. Multi-level decomposition refers to dividing the frequency-domain signal into multiple sub-bands according to the frequency range (such as low frequency 0-1kHz, mid-frequency 1-10kHz, and high frequency 10-100kHz) to conduct in-depth analysis of the characteristics of each frequency band. The energy distribution of sub-signals reflects the degree of concentration of signal energy in different frequency bands. The energy proportion refers to the percentage of energy in each frequency band to the total energy. The center frequency represents the frequency point where the energy of the band is concentrated, and the bandwidth measures the width of the frequency range of the band. The energy proportion, center frequency, and bandwidth of each frequency band are used as components of the disturbance feature vector, which is used to quantify the characteristics of the parasitic capacitance disturbance signal.

[0031] Perform an FFT transform on the time-domain parasitic capacitance perturbation signal, and then perform wavelet packet decomposition (e.g., db4 wavelet, decomposition in 3 layers) on the frequency-domain signal to obtain 8 sub-bands (2 3 =8), obtain the energy proportion of each sub-band signal; then extract the center frequency and bandwidth of each band: form a disturbance feature vector; convert the time-domain parasitic capacitance disturbance signal into a frequency-domain feature vector, realize time-frequency conversion through FFT, and can intuitively observe the frequency component distribution of the signal; perform multi-level decomposition on the frequency domain signal, which can capture signal details with finer granularity, and further refine the frequency band analysis through multi-level decomposition, thereby locating the interference source of a specific frequency, thus effectively avoiding the omission or misjudgment of the interference source, and improving the accuracy and reliability of signal processing.

[0032] Furthermore, the method of this application includes extracting the disturbance feature vector and performing time-frequency domain synchronous correlation analysis with the touch signal to determine the signal interference intensity and signal interference delay of the parasitic capacitance disturbance signal on the touch signal.

[0033] The touch signal and the disturbance feature vector are converted into a time-frequency image using short-time Fourier transform. In the time-frequency image, a time-frequency domain synchronization correlation matrix is ​​obtained based on the Pearson correlation coefficient of the time segment-frequency region. The maximum correlation coefficient value and the variance of the correlation coefficient distribution are extracted from the time-frequency domain synchronization correlation matrix as quantitative indicators of the signal interference intensity and signal interference delay.

[0034] Specifically, the Short-Time Fourier Transform (STFT) is a time-frequency analysis method that decomposes a non-stationary signal into multiple short-time stationary segments through windowing, generating a time-frequency image (a three-dimensional time-frequency-amplitude graph) that visually displays the changes in the signal's frequency components over time. The Pearson correlation coefficient measures the degree of linear correlation between two variables, with a value range of [-1, 1]. Here, it is used to calculate the correlation between time segments and frequency regions in the time-frequency image. The time-frequency domain synchronization correlation matrix is ​​a two-dimensional matrix where rows represent time segments and columns represent frequency regions. The matrix elements are the Pearson correlation coefficients for the corresponding spatiotemporal regions, reflecting the synchronicity of the touch signal and the disturbance signal in the time-frequency domain. The maximum correlation coefficient value reflects the strongest interference intensity, and the variance of the correlation coefficient distribution reflects the concentration of interference in the time-frequency domain. The larger the variance, the more dispersed the interference and the more complex the time delay characteristics.

[0035] STFT transformation is performed on the touch signal and disturbance feature vector. Specifically, the time-frequency image is divided into 10 segments (50ms each) along the time axis and 5 regions along the frequency axis (low frequency 0-1kHz, mid frequency 1-5kHz, high frequency 5-10kHz, ultra-high frequency 10-20kHz, and extremely high frequency 20-50kHz). The Pearson correlation coefficient of each time segment-frequency region pair is evaluated to form a time-frequency domain synchronization correlation matrix. The time-frequency domain features are transformed into quantifiable interference indicators: the maximum correlation coefficient value directly reflects the interference intensity; the variance of the correlation coefficient distribution is used to assess the dispersion of interference and to indicate the existence of multiple interference sources. By locating the time-frequency region where the maximum correlation coefficient is located, the interference delay can be accurately determined, providing time-dimensional fault characteristics for subsequent failure analysis.

[0036] Furthermore, based on the signal interference intensity and signal interference delay, and combined with the physical defect type, a multi-point correlation mapping is performed. The method of this application includes:

[0037] A defect feature library is established, including physical defect types such as scratches, bubbles, and electrode breaks. The parasitic capacitance disturbance features, signal interference intensity features, and signal interference delay features corresponding to each physical defect type are classified and stored. For candidate defect types in the defect feature library, matching physical defect types are selected by combining the waveform distortion of the touch signal and the correlation of the contact force, and multi-point correlation mapping is performed.

[0038] Specifically, physical defect types refer to the forms of physical damage that may occur to the ultra-thin touch switch color film, such as surface scratches, internal bubbles, and electrode breakage. The defect feature library is a pre-established database that stores the key electrical performance characteristics corresponding to each physical defect, including: parasitic capacitance disturbance characteristics: for example, scratches can cause abnormal increases in signal energy in specific high-frequency bands (such as 10-20kHz) (15%-30% higher than normal); signal interference intensity characteristics: electrode breakage may cause the interference intensity to increase significantly when the contact force exceeds 2N (from 0.3 to above 0.8); signal interference delay characteristics: bubble defects are usually accompanied by significant signal delay (such as 30-50ms, 2-5 times longer than normal); waveform distortion includes amplitude attenuation of the touch signal (such as exceeding 25%), abnormal waveform oscillations, or increased harmonic components; contact force correlation refers to the law of change of interference characteristics with the pressure applied.

[0039] By analyzing the electrical performance data of known defective samples, such as testing a color filter with a 0.2mm scratch, it was found that, within the same frequency band, the signal energy percentage of the defective sample exceeded the average signal energy percentage of the normal sample (e.g., 22% > 5%), and the interference intensity increased linearly with increasing force (slope 0.3 / N). These features were categorized and stored in a defect feature library, forming feature labels for each defect (e.g., scratches correspond to increased high-frequency energy + positive correlation with force).

[0040] When testing unknown samples, the frequency energy distribution, interference intensity, time delay data, and touch signal waveform distortion degree of the parasitic capacitance disturbance signal are first extracted. Then, these data are compared with candidate types (scratches, bubbles, electrode breakage) in the defect feature library. Through multi-dimensional feature matching (high-frequency energy + force correlation + amplitude attenuation), the scratch defect is identified, and an association mapping between interference features and scratch defects is established. By associating interference features with physical defects at multiple points, misjudgment by a single indicator can be avoided (such as confusing bubbles with electrode oxidation based solely on time delay), thereby improving the accuracy of defect identification.

[0041] Furthermore, for the candidate defect types in the defect feature library, the method of this application includes:

[0042] Based on the quantitative indicators of signal interference intensity and signal interference delay, and the characteristics of parasitic capacitance disturbance, a test feature vector is configured; based on the test feature vector and the cosine similarity of the feature vectors under various physical defect types in the defect feature library, a similarity threshold is set; traversing various physical defect types in the defect feature library, one or more physical defect types with a feature vector cosine similarity greater than the similarity threshold are marked as candidate defect types.

[0043] Specifically, the test feature vector is a multi-dimensional data set that integrates signal interference intensity, signal interference delay, and parasitic capacitance disturbance characteristics (such as the energy proportion of each frequency band, center frequency, etc.) to characterize the abnormal electrical performance characteristics of the current test sample; cosine similarity is an algorithm that measures the similarity of two vector directions, with a value range of [-1, 1]. The closer the value is to 1, the higher the similarity of the feature vectors; the similarity threshold is a set judgment standard (such as 0.7) used to screen physical defect types that match the characteristics of the test sample; candidate defect types refer to physical defects that are preliminarily determined to exist after similarity calculation.

[0044] The quantitative indicators obtained from a certain test (such as signal interference strength of 0.8 and interference delay of 30ms) and parasitic capacitance disturbance characteristics (such as energy proportion of 20% in the 15-20kHz frequency band and center frequency of 18kHz) are integrated into a test feature vector, for example, represented as [0.8, 30, 0.2, 18]. Then, based on historical test data and defect diagnosis experience, a cosine similarity threshold of 0.7 is set. That is, when the cosine value of the test feature vector and a certain type of feature vector in the defect feature library is ≥0.7, the two are considered to have high similarity. All physical defect types (such as scratches, bubbles, and electrode breakage) in the defect feature library are traversed to determine the cosine similarity between the test feature vector and each type of feature vector. The next step is failure mechanism analysis, and the defect type is quickly screened through quantitative calculation. In the case of multiple physical defect types mixed on the ultra-thin touch switch color film, by setting a reasonable cosine similarity threshold, the false detection rate can be controlled within a certain range, while avoiding missed detection.

[0045] Furthermore, by identifying critical failure paths through failure mechanism analysis, the method of this application includes:

[0046] A defect propagation path model is constructed based on the matching physical defect types corresponding to the multi-point association mapping; the electrical performance test parameters are used as evidence nodes of the defect propagation path model; and the critical failure path is determined by analyzing the posterior probability of the defect propagation nodes corresponding to the matching physical defect types.

[0047] Specifically, the defect propagation path model is a causal relationship model built based on physical defect types, used to describe the evolution process of a defect from its generation to performance failure (e.g., surface scratches → increased parasitic capacitance → enhanced signal interference → touch response delay); evidence nodes refer to observable electrical performance test parameters in the model (such as test voltage, impedance value within the frequency scanning range), used as evidence to infer the defect propagation path; posterior probability is the probability of a defect propagation node (such as increased parasitic capacitance and enhanced signal interference) occurring given known electrical performance test data, reflecting the credibility of the node in the causal chain.

[0048] First, for each matched physical defect type, a corresponding defect propagation path model is constructed. Taking a scratch as an example, the model path can be represented as: initial defect (scratch) → intermediate node 1 (abnormal increase in parasitic capacitance) → intermediate node 2 (high-frequency signal interference intensity exceeds 0.6) → failure (accidental touch). Electrical performance test parameters are embedded into the model as evidence nodes. For example, the impedance fluctuation amplitude measured in the frequency scanning range (2kHz-20kHz) serves as evidence of abnormal parasitic capacitance. The posterior probability of each defect propagation node is obtained using Bayesian network probabilistic inference. If an interference intensity of 0.75 is detected simultaneously (interference intensity threshold 0.6), the posterior probability of the signal interference enhancement node is further increased. By selecting the path with the highest posterior probability (such as scratch → increased parasitic capacitance → enhanced signal interference → touch failure, with the mean posterior probability of each node sorted from largest to smallest, and the path formed by connecting the nodes at the top), the critical failure path is identified.

[0049] From defect type matching to failure mechanism analysis, if the posterior probability of a node with a sudden change in contact resistance is 40%, while the posterior probability of a node with dielectric layer damage caused by partial discharge reaches 90%, it indicates that the main cause of failure is not direct fracture, but secondary discharge damage triggered by fracture. Therefore, the critical failure path is revised to (before revision: critical failure path: electrode fracture → sudden change in contact resistance → abnormal signal transmission → touch failure): electrode fracture → partial discharge → dielectric layer damage → signal attenuation. By combining evidence nodes with posterior probabilities, secondary interference factors can be eliminated, improving the accuracy of locating the critical failure path.

[0050] Furthermore, the method of this application includes writing the critical failure path into a failure alert signal:

[0051] A virtual touch switch color filter model containing the critical failure path is set up using digital twin technology; the virtual touch switch color filter model is subjected to multi-dimensional visualization rendering; a data interaction channel is established between the virtual touch switch color filter model and the test platform, and the critical failure path is uploaded to the virtual touch switch color filter model for failure risk warning.

[0052] Specifically, a digital twin refers to a virtual model created through digital means that perfectly corresponds to the real ultra-thin touch switch color film, capable of mapping the state and performance of the physical entity in real time. The virtual touch switch color film model is a three-dimensional virtual entity built based on digital twin technology, containing parameterized representations of key failure paths (such as the location and depth of scratches, and numerical changes in parasitic capacitance). Multi-dimensional visualization rendering refers to intuitively displaying the model's state from multiple dimensions, including geometric structure (such as film thickness distribution), electrical performance parameters (such as impedance curves), and failure path dynamics (such as the propagation process of interference signals), in the form of graphics, animations, and data dashboards. The data interaction channel is a real-time data transmission link connecting the virtual model and the test platform, used to synchronize test data (such as measured interference intensity and latency) and failure analysis results. Failure risk warning is achieved by simulating the development trend of failure paths through the virtual model and issuing early warning information.

[0053] Based on the identified critical failure paths (e.g., scratches → increased parasitic capacitance → enhanced signal interference → touch failure), a virtual touch switch color filter model was constructed using CAD software and electromagnetic simulation tools (e.g., HFSS). The geometric parameters of the scratches (2mm length, 5μm depth) and the corresponding parasitic capacitance values ​​(25pF higher than normal areas) were precisely set in the virtual touch switch color filter model. Then, the virtual touch switch color filter model was subjected to multi-dimensional visualization rendering: Geometric dimension: The internal structure of the film layer was displayed with a semi-transparent effect, showing the scratch location; Electrical performance dimension: Real-time impedance-frequency curves were dynamically plotted, with blue shading indicating normal parameter ranges and yellow curves representing abnormal values; Failure path dimension: The propagation process of interference signals from the scratched area to the touch electrode was demonstrated through animation, with arrows indicating the direction of energy leakage.

[0054] A data interface is established between the virtual touch switch color filter model and the test platform. The measured electrical performance parameters are synchronized to the virtual touch switch color filter model in real time. The virtual touch switch color filter model is based on a mathematical model of the key failure path (such as the relationship between scratch depth and parasitic capacitance). Failure risk level: combined with historical data prediction, if the current trend continues, the probability of touch failure will increase after a preset time period as shown by the virtual touch switch color filter model. Multi-dimensional rendering can quickly locate the root cause of failure. Combined with dynamic simulation to predict the failure development trend, passive maintenance is transformed into proactive prevention.

[0055] Furthermore, by uploading the critical failure path to the virtual touch switch color filter model, the method of this application also includes:

[0056] The information of each node in the critical failure path is encoded to form a digital label containing the defect type, location coordinates, and degree of impact. The digital label is then used to map and highlight the virtual touch switch color filter model. The touch signal and contact force data of the next node are uploaded, a defect propagation evaluation function is constructed, the impact of the critical failure path on the touch performance of the next node is quantitatively evaluated, and the critical contact force threshold is determined.

[0057] Specifically, the information at each node in the critical failure path refers to the elements that constitute the various links in the failure chain, such as surface scratches (defect type), the upper left corner area of ​​the film layer (location coordinates), and increased parasitic capacitance (degree of impact); digital tags are strings or QR codes generated by encoding this information according to specific rules, containing parameters such as defect type, location coordinates, and degree of impact; mapping and highlighting refers to visually presenting the defect location and impact corresponding to the digital tags in the virtual model using color flashing, 3D annotation, etc.; the defect propagation evaluation function is used to calculate the quantitative impact of the critical failure path on the touch performance of the next node (such as from increased parasitic capacitance to enhanced signal interference), for example, by deriving the relationship between interference intensity and contact force through regression analysis; the critical contact force threshold is the minimum contact force value that triggers the next stage of failure, exceeding which will lead to a significant deterioration in touch performance.

[0058] Information is encoded for each node in the identified critical failure path (e.g., electrode breakage → increased contact resistance → signal attenuation); taking the node with increased contact resistance as an example, a digital tag is generated; in the virtual touch switch color film model, the digital tag is mapped to the three-dimensional structure; through dynamic rendering, when the mouse hovers, the model automatically highlights the impact range of the node on the surrounding circuit (e.g., using blue shading to indicate the signal attenuation area), uploads the test data of the next node (e.g., the touch signal waveform when the contact force gradually increases from 1N to 3N), calls the defect propagation evaluation function (related to contact force and the proportion of resistance increase), compares the performance impact values ​​under different forces, determines the critical contact force threshold (e.g., 2.5N), at this time, the corresponding force adjustment slider in the virtual model automatically marks the warning line and pops up a warning: exceeding 2.5N will trigger the risk of signal failure.

[0059] In summary, the beneficial effects of the embodiments of this application are:

[0060] By placing an ultra-thin touch switch color film on the test platform, basic material parameters are uploaded to the test platform. The test platform is then configured with electrical performance test parameters based on these parameters. The test platform is connected to a signal generator and an oscilloscope. After initializing the test platform with the electrical performance test parameters, touch signals and contact force data are introduced. Parasitic capacitance disturbance signals are recorded during the electrical performance test. The frequency components of the parasitic capacitance disturbance signals are decomposed, disturbance feature vectors are extracted, and time-frequency domain synchronous correlation analysis is performed with the touch signals to determine the signal interference intensity and signal interference delay of the parasitic capacitance disturbance signals on the touch signals. Based on the signal interference intensity and signal interference delay, multi-point correlation mapping is performed in conjunction with the physical defect type. Subsequently, critical failure paths are determined through failure mechanism analysis. The critical failure paths are then written into the failure warning signal. This application provides a performance testing method and system based on ultra-thin touch switch color film. It achieves the technical effect of automatically adjusting test parameters according to the characteristics of different color film materials by constructing material parameter-driven adaptive configuration of electrical performance test parameters, thereby improving test accuracy and effectiveness. At the same time, it breaks through the limitations of traditional single-point testing, accurately quantifies signal interference characteristics, deeply understands the signal interference situation, and combines failure mechanism analysis to quickly locate the root cause of the fault and reduce the probability of failure.

[0061] Example 2

[0062] Based on the same inventive concept as the performance testing method based on the ultra-thin touch switch color film in the foregoing embodiments, such as Figure 2 As shown in the embodiment of this application, a performance testing system based on an ultra-thin touch switch color filter is provided, wherein the system includes:

[0063] The performance testing module M100 is used to place the ultra-thin touch switch color film on the testing platform and upload basic material parameters to the testing platform; the testing platform configures electrical performance testing parameters based on the basic material parameters.

[0064] The initialization configuration module M200 is used to connect the test platform to the signal generator and oscilloscope; after initializing the test platform with the electrical performance test parameters, it introduces touch signals and contact force data.

[0065] The frequency decomposition module M300 is used to record the parasitic capacitance disturbance signal during the electrical performance test, decompose the frequency components of the parasitic capacitance disturbance signal, extract the disturbance feature vector, perform time-frequency domain synchronous correlation analysis with the touch signal, and determine the signal interference intensity and signal interference delay of the parasitic capacitance disturbance signal on the touch signal.

[0066] The failure alert module M400 is used to perform multi-point correlation mapping based on the signal interference intensity and signal interference delay, combined with the physical defect type. Then, it determines the critical failure path through failure mechanism analysis and writes the critical failure path into the failure alert signal.

[0067] Furthermore, the performance testing module M100 is used to perform the following methods:

[0068] The basic material parameters include film thickness, electrode material, and touch sensing sensitivity, and an equivalent circuit model of the ultra-thin touch switch color filter is constructed. Based on the equivalent circuit model of the ultra-thin touch switch color filter located on the test platform, the impedance characteristics and capacitive reactance characteristics at different frequencies are obtained. Through the impedance characteristics and capacitive reactance characteristics at different frequencies, the test voltage range, test current threshold, and frequency scanning interval are determined, and the electrical performance test parameters are set.

[0069] Furthermore, the frequency decomposition module M300 is used to perform the following method:

[0070] The parasitic capacitance disturbance signal in the time domain is converted into a frequency domain signal using FFT; the frequency domain signal is decomposed into multiple layers to obtain the energy distribution of sub-signals in each frequency band, and the energy proportion, center frequency, and bandwidth of each frequency band are extracted as constituent elements of the disturbance feature vector.

[0071] Furthermore, the frequency decomposition module M300 is also used to perform the following method:

[0072] The touch signal and the disturbance feature vector are converted into a time-frequency image using short-time Fourier transform. In the time-frequency image, a time-frequency domain synchronization correlation matrix is ​​obtained based on the Pearson correlation coefficient of the time segment-frequency region. The maximum correlation coefficient value and the variance of the correlation coefficient distribution are extracted from the time-frequency domain synchronization correlation matrix as quantitative indicators of the signal interference intensity and signal interference delay.

[0073] Furthermore, the failure reminder module M400 is used to perform the following method:

[0074] A defect feature library is established, including physical defect types such as scratches, bubbles, and electrode breaks. The parasitic capacitance disturbance features, signal interference intensity features, and signal interference delay features corresponding to each physical defect type are classified and stored. For candidate defect types in the defect feature library, matching physical defect types are selected by combining the waveform distortion of the touch signal and the correlation of the contact force, and multi-point correlation mapping is performed.

[0075] Furthermore, the failure reminder module M400 is also used to perform the following method:

[0076] Based on the quantitative indicators of signal interference intensity and signal interference delay, and the characteristics of parasitic capacitance disturbance, a test feature vector is configured; based on the test feature vector and the cosine similarity between it and the feature vectors of various physical defect types in the defect feature library, a similarity threshold is set.

[0077] Traverse the various physical defect types in the defect feature library and mark one or more physical defect types whose feature vector cosine similarity is greater than the similarity threshold as candidate defect types.

[0078] Furthermore, the failure reminder module M400 is also used to perform the following method:

[0079] A defect propagation path model is constructed based on the matching physical defect types corresponding to the multi-point association mapping; the electrical performance test parameters are used as evidence nodes of the defect propagation path model; and the critical failure path is determined by analyzing the posterior probability of the defect propagation nodes corresponding to the matching physical defect types.

[0080] Furthermore, the failure reminder module M400 is also used to perform the following method:

[0081] A virtual touch switch color filter model containing the critical failure path is set up using digital twin technology; the virtual touch switch color filter model is subjected to multi-dimensional visualization rendering; a data interaction channel is established between the virtual touch switch color filter model and the test platform, and the critical failure path is uploaded to the virtual touch switch color filter model for failure risk warning.

[0082] Furthermore, the failure reminder module M400 is also used to perform the following method:

[0083] The information of each node in the critical failure path is encoded to form a digital label containing the defect type, location coordinates, and degree of impact. The digital label is then used to map and highlight the virtual touch switch color filter model. The touch signal and contact force data of the next node are uploaded, a defect propagation evaluation function is constructed, the impact of the critical failure path on the touch performance of the next node is quantitatively evaluated, and the critical contact force threshold is determined.

[0084] In summary, any step can be stored as a computer instruction or program in an unrestricted computer memory and can be called and recognized by an unrestricted computer processor; no further restrictions are imposed here.

[0085] Furthermore, the above technical solutions only embody the preferred technical solutions of the embodiments of this application. Any changes that those skilled in the art may make to certain parts of these solutions embody the novel principles of the embodiments of this application. Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of this application.

Claims

1. A performance testing method based on an ultra-thin touch switch color filter, characterized in that, The method includes: An ultra-thin touch switch color film is placed on a test platform, and basic material parameters are uploaded to the test platform; the test platform is configured with electrical performance test parameters based on the basic material parameters. The test platform is connected to a signal generator and an oscilloscope; after initializing the test platform with the electrical performance test parameters, touch signals and contact force data are introduced. Record the parasitic capacitance disturbance signal during the electrical performance test, decompose the frequency components of the parasitic capacitance disturbance signal, extract the disturbance feature vector, perform time-frequency domain synchronous correlation analysis with the touch signal, and determine the signal interference intensity and signal interference delay of the parasitic capacitance disturbance signal on the touch signal. Based on the signal interference intensity and signal interference delay, multi-point correlation mapping is performed in conjunction with the physical defect type. Then, the critical failure path is determined through failure mechanism analysis. The critical failure path is written into the failure alert signal.

2. The performance testing method based on an ultra-thin touch switch color filter as described in claim 1, characterized in that, The basic material parameters include film thickness, electrode material, and touch sensing sensitivity, and an equivalent circuit model of the ultra-thin touch switch color film is constructed. Based on the equivalent circuit model of the ultra-thin touch switch color film located on the test platform, the impedance characteristics and capacitive reactance characteristics at different frequencies are obtained. By analyzing the impedance and capacitive reactance characteristics at different frequencies, the test voltage range, test current threshold, and frequency scan interval are determined, and the electrical performance test parameters are set.

3. The performance testing method based on an ultra-thin touch switch color filter as described in claim 1, characterized in that, The method includes recording parasitic capacitance disturbance signals during electrical performance testing, decomposing the frequency components of the parasitic capacitance disturbance signals, and extracting disturbance feature vectors. The parasitic capacitance perturbation signal in the time domain is converted into a frequency domain signal using FFT; The frequency domain signal is decomposed into multiple layers to obtain the energy distribution of sub-signals in each frequency band, and the energy proportion, center frequency, and bandwidth of each frequency band are extracted as constituent elements of the disturbance feature vector.

4. The performance testing method based on an ultra-thin touch switch color filter as described in claim 3, characterized in that, Extracting the perturbation feature vector and performing time-frequency domain synchronization correlation analysis with the touch signal to determine the signal interference intensity and signal interference delay of the parasitic capacitance perturbation signal on the touch signal, the method includes: The touch signal and the disturbance feature vector are converted into a time-frequency image using short-time Fourier transform; In the time-frequency image, the time-frequency domain synchronization correlation matrix is ​​obtained based on the Pearson correlation coefficient of the time segment-frequency region; The maximum correlation coefficient value and the variance of the correlation coefficient distribution are extracted from the time-frequency domain synchronization correlation matrix and used as quantitative indicators of the signal interference intensity and signal interference delay.

5. The performance testing method based on an ultra-thin touch switch color filter as described in claim 4, characterized in that, Based on the signal interference intensity and signal interference delay, and combined with the physical defect type, a multi-point correlation mapping is performed, the method comprising: Establish a defect feature library for physical defect types including scratches, bubbles, and electrode fractures, and classify and store the parasitic capacitance disturbance features, signal interference intensity features, and signal interference delay features corresponding to each physical defect type. For candidate defect types in the defect feature library, the waveform distortion of the touch signal and the correlation of the contact force are combined to select matching physical defect types and perform multi-point correlation mapping.

6. The performance testing method based on an ultra-thin touch switch color filter as described in claim 5, characterized in that, For candidate defect types in the defect feature library, the method includes: Based on the quantitative indicators of signal interference intensity and signal interference delay, and the characteristics of parasitic capacitance disturbance, a test feature vector is configured. Based on the test feature vector and the cosine similarity with the feature vectors of various physical defect types in the defect feature library, a similarity threshold is set. Traverse the various physical defect types in the defect feature library and mark one or more physical defect types whose feature vector cosine similarity is greater than the similarity threshold as candidate defect types.

7. The performance testing method based on an ultra-thin touch switch color filter as described in claim 5, characterized in that, The method for identifying critical failure paths through failure mechanism analysis includes: Based on the matching physical defect types corresponding to the multi-point correlation mapping, a defect propagation path model is constructed; the electrical performance test parameters are used as evidence nodes of the defect propagation path model. Based on the analysis of the posterior probability of the defect propagation nodes corresponding to the matched physical defect types, the critical failure path is proposed.

8. The performance testing method based on an ultra-thin touch switch color filter as described in claim 1, characterized in that, The method of writing the critical failure path into a failure alert signal includes: A virtual touch switch color filter model containing the key failure path is set up using a digital twin; the virtual touch switch color filter model is then subjected to multi-dimensional visualization rendering. A data interaction channel is established between the virtual touch switch color filter model and the test platform. The critical failure path is uploaded to the virtual touch switch color filter model for failure risk warning.

9. The performance testing method based on an ultra-thin touch switch color filter as described in claim 8, characterized in that, The method further includes uploading the critical failure path to the virtual touch switch color filter model: The information of each node in the critical failure path is encoded to form a digital label containing the defect type, location coordinates, and degree of impact; In the virtual touch switch color filter model, the digital labels are used for mapping and highlighting; Upload the touch signal and contact force data of the next node, construct a defect propagation evaluation function, quantify the impact of the critical failure path on the touch performance of the next node, and determine the critical contact force threshold.

10. A performance testing system based on an ultra-thin touch switch color filter, characterized in that, The system is used to implement the performance testing method based on an ultra-thin touch switch color film according to any one of claims 1-9, the system comprising: The performance testing module is used to place the ultra-thin touch switch color film on the testing platform and upload basic material parameters to the testing platform; the testing platform configures electrical performance testing parameters based on the basic material parameters. An initialization configuration module is used to connect the test platform to the signal generator and oscilloscope; after initializing the test platform with the electrical performance test parameters, touch signals and contact force data are introduced. The frequency decomposition module is used to record the parasitic capacitance disturbance signal during the electrical performance test, decompose the frequency components of the parasitic capacitance disturbance signal, extract the disturbance feature vector, perform time-frequency domain synchronous correlation analysis with the touch signal, and determine the signal interference intensity and signal interference delay of the parasitic capacitance disturbance signal on the touch signal. The failure alert module is used to perform multi-point correlation mapping based on the signal interference intensity and signal interference delay, combined with the physical defect type. Then, it determines the critical failure path through failure mechanism analysis and writes the critical failure path into the failure alert signal.

Images