Thermal printing head dead pixel type identification and positioning method and system

Abstract

The invention relates to the technical field of defective pixel testing, and discloses a thermal printing head defective pixel type identification and positioning method and system, and the method comprises the steps: applying two stress pulses to a target element of a thermal printing head, obtaining the multi-dimensional data in the application period of the two stress pulses in real time, and enabling the target element to be a single heating point of a head piece in the thermal printing head; calculating a stress response voltage, a stress response current and two mutual interference voltages in the preprocessed multi-dimensional data to obtain a time-varying attenuation rate of target element impedance during the application period of the first stress pulse, and analyzing a power parameter in the preprocessed multi-dimensional data to obtain a hysteresis energy loss ratio; multi-dimensional data are acquired through two stress pulses, and data such as stress response voltage, current and mutual interference voltage are combined for analysis, so that crosstalk noise is effectively stripped, characteristic offset misjudgment is avoided, defective pixel types are accurately distinguished, positioning precision is improved, ultrahigh-resolution printing head testing is adapted, and testing reliability is enhanced.

Description

Technical Field

[0001] This invention relates to the field of defective pixel testing technology, specifically to a method and system for identifying and locating defective pixel types in a thermal printhead. Background Technology

[0002] Currently, when identifying and locating dead pixels in thermal printheads, a combination of a precision probe station and a parameter analyzer is typically used. Under power-on conditions, the resistance, current-voltage characteristic curves of the printhead heating element are scanned point by point or in groups. By comparing the measured values ​​with a preset threshold range, it is determined whether a point is an open circuit or a short circuit hard fault, thereby achieving the identification and location of dead pixels.

[0003] However, as printheads develop towards ultra-high resolution and miniaturization, the resistive size and spacing of thermal printheads have drastically decreased. As a result, during testing, the parasitic capacitive coupling and electromagnetic crosstalk effects between adjacent test probes become extremely significant. This crosstalk introduces non-negligible noise when acquiring weak resistance changes, leading to distortion of the current-voltage curve. Existing technologies struggle to remove this crosstalk component, easily misjudging characteristic shifts caused by crosstalk as component degradation and misidentifying them as fault points, thus reducing the positioning accuracy of the test. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for identifying and locating defective pixel types in thermal printheads, thus solving the aforementioned problems.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution: A method for identifying and locating defective pixel types in a thermal printhead, comprising: Step S1: Apply two stress pulses to the target element of the thermal printhead and acquire multidimensional data in real time during the application of the two stress pulses. The target element is a single heating point of the printhead plate in the thermal printhead. Step S2: Calculate the stress response voltage, stress response current, and two mutual interference voltages in the preprocessed multidimensional data to obtain the time-varying decay rate of the target element impedance during the first stress pulse application period, and analyze the power parameters in the preprocessed multidimensional data to obtain the hysteresis energy loss ratio. Step S3: Construct a recovery trajectory curve based on the recovery voltage and recovery current in the preprocessed multidimensional data, and analyze the recovery trajectory curve to obtain the recovery trajectory stability coefficient; Step S4: Construct test points based on time-varying decay rate, hysteresis energy loss ratio and recovery trajectory stability coefficient, and calculate the geometric deviation vector between the test points and the benchmark healthy reference area to obtain the tangent space projection length and normal penetration depth. Step S5: Perform a joint judgment on the tangent space projection length and normal penetration depth to obtain the bad spot identification and location command.

[0006] Furthermore, multidimensional data includes: During the application of the first stress pulse, the stress response voltage across the target element and the stress response current flowing through it, as well as the mutual interference voltage between two adjacent target elements, are acquired. Mutual interference voltage is the transient interference voltage induced on adjacent components through parasitic capacitance when a stress pulse is applied to the target component. Furthermore, the recovery voltage and recovery current during the recovery period after the second stress pulse ends are acquired synchronously in real time, and the instantaneous power during the application of the first and second stress pulses is acquired in real time, respectively.

[0007] Furthermore, the order in which the stress pulses are applied includes: Ten seconds after the first stress pulse ends, a second stress pulse is applied. Furthermore, the voltage amplitude of the stress pulse is higher than the rated operating voltage of the target component.

[0008] Furthermore, the stress response voltage, stress response current, and two mutual interference voltages in the preprocessed multidimensional data are calculated to obtain the time-varying decay rate of the target element impedance during the application of the first stress pulse. The power parameters in the preprocessed multidimensional data are also analyzed to obtain the hysteresis energy loss ratio, including: The field gradient divergence is obtained by analyzing the instantaneous changes of stress response voltage, stress response current and mutual disturbance voltage in the target element and its adjacent elements in the multidimensional data. The intrinsic instability index is obtained by decomposing the stress response voltage and stress response current in the multidimensional data. The time-varying decay rate is obtained by fusing the field gradient divergence and the intrinsic instability index. Based on the two instantaneous powers in the preprocessed multidimensional data, the peak values ​​of the instantaneous entropy production rate of the two stress pulses are analyzed to obtain the entropy production mutation degree. By analyzing the offset and hysteresis of the two instantaneous powers, the path offset margin is obtained; The entropy production mutation degree and path offset margin are calculated together to generate the hysteresis energy loss ratio.

[0009] Furthermore, a recovery trajectory curve is constructed based on the recovery voltage and recovery current in the preprocessed multidimensional data, and the recovery trajectory curve is analyzed to obtain the recovery trajectory stability coefficient, including: The time-series data of recovery voltage and recovery current are mapped to a recovery trajectory curve. The integral of the curl of adjacent state points on the time axis is calculated to obtain the convergence heterogeneity. The neighborhood of the endpoint of the recovered trajectory curve is regarded as a transient attractor. The change curve of the transient attractor is calculated to obtain the topological robustness index. The convergence heterogeneity and topological robustness index are calculated to obtain the stability coefficient of the recovered trajectory.

[0010] Furthermore, test points are constructed based on the time-varying decay rate, hysteresis energy loss ratio, and recovery trajectory stability coefficient. The geometric deviation vector between the test points and the baseline healthy reference area is calculated to obtain the tangent space projection length and normal penetration depth, including: The time-varying decay rate, hysteresis energy loss ratio, and recovery trajectory stability coefficient are used as test points, and analyzed with the baseline health reference area to generate a defect topological potential energy scalar. Based on the scalar adjustment of the boundary of the benchmark healthy reference region using the defect topological potential energy scalar, a dynamic topological kernel of the healthy domain is generated. Starting from the dynamic topology kernel of the healthy domain, the shortest geodesic path from the test point to the dynamic topology kernel of the healthy domain is calculated, and then the tangent vector of the shortest geodesic path is extracted to generate the covariant curvature distortion vector.

[0011] Furthermore, test points are constructed based on the time-varying decay rate, hysteresis energy loss ratio, and recovery trajectory stability coefficient. The geometric deviation vector between the test points and the baseline healthy reference area is calculated to obtain the tangent space projection length and normal penetration depth. This also includes: The covariant curvature distortion vector is projected onto the tangent space at the dynamic topological kernel of the healthy domain, and the projection modulus is corrected using the defect topological potential energy scalar to obtain the projection length of the tangent space. The latent curvature penetration potential is calculated based on the covariant curvature distortion vector, the defect topological potential energy scalar, and the dynamic topological kernel of the healthy domain. After mapping the latent curvature penetration potential, the normal penetration depth is generated.

[0012] Furthermore, the calculation process for the baseline health reference area includes: Multiple healthy target components are obtained and used as a sample set. The sample set is subjected to the same tests from step S1 to step S3 to obtain the standard time-varying decay rate, recovery trajectory stability coefficient and hysteresis energy loss ratio. The baseline health reference region is obtained by calculating the standard time-varying decay rate, recovery trajectory stability coefficient, and hysteresis energy loss ratio.

[0013] Furthermore, by jointly judging the tangent space projection length and normal penetration depth, a defect identification and location instruction is obtained, including: The sample set is subjected to the same tests from step S1 to step S4 to obtain the standard tangent space projection length and normal penetration depth. The standard tangent space projection length and normal penetration depth are calculated to obtain the first threshold and the second threshold. By comparing the tangent space projection length and normal penetration depth with the first threshold and the second threshold, a bad pixel identification and location command is obtained.

[0014] Furthermore, a thermal printhead defect type identification and location system, applied to the aforementioned thermal printhead defect type identification and location method, includes: The pulse test unit is used to apply two stress pulses to the target element of the thermal printhead and acquire multidimensional data in real time during the application of the two stress pulses. The target element is a single heating point of the printhead plate in the thermal printhead. The data analysis unit is used to calculate the stress response voltage, stress response current and two mutual interference voltages in the preprocessed multidimensional data, obtain the time-varying decay rate of the target element impedance during the first stress pulse application, and analyze the power parameters in the preprocessed multidimensional data to obtain the hysteresis energy loss ratio. The data calculation unit is used to construct the recovery trajectory curve based on the recovery voltage and recovery current in the preprocessed multidimensional data, and to analyze the recovery trajectory curve to obtain the recovery trajectory stability coefficient. The deviation analysis unit is used to construct test points based on the time-varying decay rate, hysteresis energy loss ratio and recovery trajectory stability coefficient, and to calculate the geometric deviation vector between the test points and the benchmark healthy reference area, thereby obtaining the tangent space projection length and normal penetration depth. The identification and positioning unit is used to collaboratively determine the tangent spatial projection length and normal penetration depth to obtain bad spot identification and positioning instructions.

[0015] In summary, the present invention has the following main beneficial effects: By applying two stress pulses and acquiring multidimensional data, information such as stress response voltage, stress response current, and crosstalk voltage is actively captured, avoiding the passive neglect of crosstalk signals in existing technologies. The time-varying decay rate is obtained by combining field gradient divergence and intrinsic instability index. The hysteresis energy loss ratio is calculated by entropy production mutation degree and path offset margin. This can accurately remove noise introduced by parasitic capacitive coupling and electromagnetic crosstalk, and distinguish between characteristic shift caused by crosstalk and actual component degradation.

[0016] After constructing and analyzing the recovery trajectory curve using recovery voltage and recovery current, the recovery trajectory stability coefficient is obtained. This allows for the capture of subtle changes in the component's recovery characteristics. Test points are then constructed, and by combining the baseline health reference area, the dynamic topology kernel of the health domain, and the covariant curvature distortion vector, the tangent space projection length and normal penetration depth are accurately calculated. This allows for an understanding of the characteristic deviations. First and second thresholds are set, and collaborative judgment can distinguish between healthy, degraded, poor contact, open circuit, and short circuit states. This avoids the limitations of single threshold comparison, adapts to the miniaturization trend of printheads, reduces the false judgment rate, improves positioning accuracy and test stability, and simultaneously achieves accurate classification of defect types. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method for identifying and locating dead pixels in a thermal printhead according to the present invention; Figure 2 This is a schematic diagram of a thermal printhead defect type identification and positioning system according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] refer to Figure 1 and 2 A method for identifying and locating defective pixel types in a thermal printhead, comprising: Step S1: Apply two stress pulses to the target element of the thermal printhead and acquire multidimensional data in real time during the application of the two stress pulses. The target element is a single heating point of the printhead plate in the thermal printhead. Step S2: Calculate the stress response voltage, stress response current, and two mutual interference voltages in the preprocessed multidimensional data to obtain the time-varying decay rate of the target element impedance during the first stress pulse application period, and analyze the power parameters in the preprocessed multidimensional data to obtain the hysteresis energy loss ratio. Step S3: Construct a recovery trajectory curve based on the recovery voltage and recovery current in the preprocessed multidimensional data, and analyze the recovery trajectory curve to obtain the recovery trajectory stability coefficient; Step S4: Construct test points based on time-varying decay rate, hysteresis energy loss ratio and recovery trajectory stability coefficient, and calculate the geometric deviation vector between the test points and the benchmark healthy reference area to obtain the tangent space projection length and normal penetration depth. Step S5: Perform a joint judgment on the tangent space projection length and normal penetration depth to obtain the bad spot identification and location command.

[0020] In one embodiment, the multidimensional data includes: During the application of the first stress pulse, the stress response voltage across the target element and the stress response current flowing through it, as well as the mutual interference voltage between two adjacent target elements, are acquired. Mutual interference voltage is the transient interference voltage induced on adjacent components through parasitic capacitance when a stress pulse is applied to the target component. Furthermore, the recovery voltage and recovery current during the recovery period after the second stress pulse ends are acquired synchronously and in real time. The recovery period is 0.5 to 5 seconds after the second stress pulse ends. The instantaneous power during the application of the first and second stress pulses is also acquired in real time.

[0021] In one embodiment, the order in which the stress pulses are applied includes: Ten seconds after the first stress pulse ends, a second stress pulse is applied. Furthermore, the voltage amplitude of the stress pulse is higher than the rated operating voltage of the target component.

[0022] In one embodiment, the voltage parameters in the preprocessed multidimensional data are calculated to obtain the time-varying decay rate of the target element impedance during the application of the first stress pulse, and the power parameters in the preprocessed multidimensional data are analyzed to obtain the hysteresis energy loss ratio, including: The field gradient divergence is obtained by analyzing the instantaneous changes of stress response voltage, stress response current, and mutual interference voltage in the target element and its adjacent elements in multidimensional data. Specifically, during the first stress pulse, for each adjacent sampling time point, the stress response voltage of the next sampling point is subtracted from the stress response voltage of the previous sampling point to obtain the instantaneous voltage change; the stress response current of the next sampling point is subtracted from the stress response current of the previous sampling point to obtain the instantaneous current change; and the instantaneous current change is divided by the instantaneous voltage change to obtain the dynamic differential conductance. Divide the mutual interference voltage on two adjacent components by the stress response current of the target component at the same time to obtain two normalized mutual interference coefficients. The normalized mutual interference coefficients are regarded as virtual potentials. Centered on the target element, the dynamic differential conductance and two virtual potentials are regarded as three discrete field node values ​​on a two-dimensional plane. The spatial difference between the central node and the two adjacent virtual potential nodes is calculated, and then convolved with the dynamic differential conductance of the central node. Finally, the spatiotemporal average of the convolution result is calculated to obtain the field gradient divergence that integrates voltage, current and spatial coupling information. The field gradient divergence is used to quantify the distortion of the local electromagnetic field.

[0023] The stress response voltage and stress response current in the multidimensional data are decomposed to obtain the intrinsic instability index. Specifically, the synchronously acquired stress response voltage time series data and stress response current time series data are converted into a complex analytic signal by Hilbert transform, and their instantaneous amplitude and phase are obtained. Thus, the original voltage and current waveforms are transformed into four characteristic curves that evolve with time, namely the characteristic curves of voltage instantaneous amplitude, voltage instantaneous phase, current instantaneous amplitude, and current instantaneous phase. The values ​​of the four characteristic curves at each sampling time are taken together as a point in a four-dimensional space. These points are connected in chronological order to form a four-dimensional phase space trajectory. In the four-dimensional phase space trajectory, a local linear embedding algorithm is used to extract the dominant oscillation mode sequence. For each extracted mode sequence, all consecutive local peak points in the mode sequence are connected to draw the amplitude decay envelope. On the envelope, the ratio of the amplitude of each peak point to the amplitude of the second peak point thereafter is calculated in sequence to obtain multiple successive decay ratios representing the short-term decay rate. The geometric mean of these successive decay ratios over the entire pulse duration is calculated to obtain a stable average decay factor. The average decay factor is then mapped to a decay rate constant with standard time dimensions. The intrinsic instability index is obtained by weighting and summing all the decay rate constants according to the square of the initial instantaneous amplitude of their corresponding mode sequences.

[0024] The time-varying decay rate is obtained by fusing the field gradient divergence and the intrinsic instability index. Specifically, the process involves: normalizing the field gradient divergence and the intrinsic instability index to the 0-1 interval, calculating their arithmetic mean to obtain the intermediate factor; subtracting the field gradient divergence from the normalized intrinsic instability index and taking its absolute value to obtain the modulation factor; and adding the intermediate factor and the modulation factor to obtain the time-varying decay rate.

[0025] Based on the two instantaneous powers in the preprocessed multidimensional data, the peak values ​​of the instantaneous entropy production rate of the two stress pulses are analyzed to obtain the entropy production mutation degree. Specifically, for the instantaneous power sequence of each pulse application process, a sliding window containing 50 sampling points before and after each sampling point is set as the center. All instantaneous powers in the sliding window are normalized to the interval between 0 and 1. The normalized instantaneous power is evenly divided into 10 intervals. The probability value of each interval is obtained by dividing the number of data points in each interval by the total number of data points. Check the probability value of each interval in turn; if the probability value of the interval is greater than zero, first calculate the logarithm of the probability value to the base 10, then multiply the logarithm by the probability value itself and add a negative sign to get the contribution value; if the probability value of the interval is zero, the contribution value is zero; add the contribution values ​​of all intervals to get the Shannon entropy value, which is the instantaneous entropy productivity at that moment. By iterating through all sampling points, the instantaneous entropy yield curves of the two stress pulses are obtained respectively. The maximum value of the two curves is taken as the first peak and the second peak. Subtracting the first peak from the second peak and then dividing by the arithmetic mean of the first and second peaks gives the entropy yield mutation degree.

[0026] The offset and hysteresis of two instantaneous power sequences are analyzed to obtain the path offset margin. Specifically, the instantaneous power of the two instantaneous power sequences is used to form a vector according to the acquisition order. The vector is set as a point in the phase space, and all points are connected in time order to form two high-dimensional trajectories. Calculate the minimum cumulative deformation energy required to achieve optimal nonlinear alignment between the second trajectory and the first trajectory. This minimum cumulative deformation energy is the path offset between the two high-dimensional trajectories. Then, calculate the optimal overall translation of the second trajectory relative to the first trajectory on the time axis, which is the lag time. Divide the path offset by the lag time, and the reciprocal of the quotient is the path offset margin. If the lag time is zero, divide the path offset by 0.1 to prevent division by zero error.

[0027] The entropy production mutation degree and path offset margin are calculated together to generate the hysteresis energy loss ratio. Specifically, this includes: calculating the mean of the entropy production mutation degree and the path offset margin to obtain the logarithmic center value; calculating the absolute value of the difference between the entropy production mutation degree and the path offset margin to obtain the logarithmic offset; and adding the logarithmic center value and the logarithmic offset to obtain the hysteresis energy loss ratio.

[0028] By extracting stress response voltage, stress response current, and crosstalk voltage, the electromagnetic field distortion is analyzed by calculating the field gradient divergence, accurately removing crosstalk components, and obtaining the intrinsic instability index by combining Hilbert transform and local linear embedding algorithm to capture weak impedance changes. At the same time, the hysteresis energy loss ratio is obtained by instantaneous entropy yield analysis and path offset margin calculation, avoiding misjudging characteristic deviations caused by crosstalk as component degradation, improving the accuracy of defect identification, reducing the false judgment rate, adapting to the testing needs of miniaturized components, and improving positioning accuracy.

[0029] In one embodiment, a recovery trajectory curve is constructed based on the recovery voltage and recovery current in the preprocessed multidimensional data, and the recovery trajectory curve is analyzed to obtain the recovery trajectory stability coefficient, including: The time-series data of recovery voltage and recovery current are mapped to a recovery trajectory curve. The integral of the curl of adjacent state points on the time axis is calculated to obtain the convergence heterogeneity. Specifically, the instantaneous value of recovery voltage is used as the abscissa in a two-dimensional rectangular coordinate system, and the instantaneous value of recovery current at the same moment is used as the ordinate. In chronological order, each data pair at each sampling moment is plotted as a point in a two-dimensional rectangular coordinate system, and all adjacent points are connected sequentially with line segments in chronological order. The resulting planar curve is the recovery trajectory curve. On the recovered trajectory curve, select three consecutive data points in each group in sequence, and calculate the two-dimensional curl between the vector formed by the first two data points and the vector formed by the last two data points. Then, calculate the absolute value of the difference between two adjacent curl values ​​in sequence to obtain the divergence of the local curl corresponding to each intermediate point. Multiply the divergence value of each local curl by its corresponding sampling time interval to obtain the infinitesimal contribution value within that time interval. Sum the infinitesimal contribution values ​​generated by all time intervals to obtain the total integral value. Divide the total integral value by the total time span from the start to the end of the recovery to obtain the convergence heterogeneity.

[0030] The endpoint neighborhood of the recovery trajectory curve is considered as a transient attractor. The change curve of this transient attractor is calculated to obtain the topological robustness index. Specifically, this includes: defining all data points within the last 20% time span of the recovery trajectory curve as the endpoint neighborhood, i.e., the sample set of the transient attractor; calculating the Euclidean distance from each point in the sample set to the equilibrium point of the recovery process, and obtaining a set of distance values, where the equilibrium point of the recovery process is the mean point of all data points within the last 10% time span of the recovery trajectory curve; Arrange these distance values ​​in chronological order to form a distance evolution sequence, which is the change curve of the transient attractor; calculate the arithmetic mean of all distance values ​​on the change curve to obtain the topological robustness index.

[0031] The stability coefficient of the recovered trajectory is obtained by calculating the convergence heterogeneity and the topological robustness index. Specifically, the topological robustness index is divided by the convergence heterogeneity to obtain the ratio; the natural logarithm of the ratio plus 1 is calculated, and the result is subjected to the inverse hyperbolic tangent function to obtain the stability coefficient of the recovered trajectory.

[0032] By constructing recovery trajectory curves using recovery voltage and recovery current, and analyzing the uniformity of trajectory convergence based on convergence heterogeneity, noise interference introduced by crosstalk is accurately removed, avoiding the influence of trajectory distortion on judgment. Combined with the analysis of transient attractor stability using topological robustness index, the essential laws of component recovery characteristics are accurately understood. The recovery trajectory stability coefficient can keenly capture subtle changes in component characteristics, distinguish between characteristic deviations caused by crosstalk and actual component degradation, reduce the false judgment rate, improve the accuracy of defect identification, adapt to the needs of ultra-high resolution printhead testing, and enhance the reliability and stability of defect location.

[0033] In one embodiment, test points are constructed based on the time-varying decay rate, hysteresis energy loss ratio, and recovery trajectory stability coefficient. The geometric deviation vector between the test points and the baseline healthy reference area is calculated to obtain the tangent space projection length and normal penetration depth, including: The test point is constructed using the time-varying decay rate, hysteresis energy loss ratio, and recovery trajectory stability coefficient, and analyzed against a baseline healthy reference region to obtain the defect topological potential energy scalar. Specifically, this involves: using the time-varying decay rate, hysteresis energy loss ratio, and recovery trajectory stability coefficient as three-dimensional coordinates to construct the test point; finding the four healthy reference points in the baseline healthy reference region that are closest to the test point in Euclidean distance; calculating the vectors pointing from the test point to these four healthy reference points; and based on the four vectors, determining a local reference plane passing through the test point through least squares fitting, and calculating the unit normal vector of this plane. For each vector, which points from the test point to its corresponding healthy reference point, this vector is denoted as the direction vector. The absolute value of the scalar product of the direction vector and the unit normal vector is calculated, which is the vertical distance. The Euclidean distance between the test point and the corresponding healthy reference point is calculated, which is the length of the direction vector, and is used as the tangential distance. The vertical distance is multiplied by 2 and then divided by the tangential distance, which is the normal curvature value along the direction vector. From the four normal curvature values, find the maximum and minimum values, and take them as the two principal curvatures respectively; multiply these two principal curvature values ​​to obtain the defect topological potential scalar.

[0034] Based on the defect topological potential energy scalar, the boundary of the benchmark healthy reference region is dynamically adjusted to generate a dynamic topological kernel for the healthy domain. Specifically, all healthy reference points in the benchmark healthy reference region are sorted in ascending order according to their Euclidean distance from the test points, and the 10% of data points with the farthest Euclidean distance are removed. The remaining set of points constitutes the dynamically adjusted boundary point set. In the boundary point set, two pairs of non-adjacent points are randomly selected. The Euclidean distance between each pair of points is calculated, as well as the length of the unique path connecting these two points in the minimum spanning tree of the boundary point set. This length is the geodesic distance. For each pair of points, the ratio of its geodesic distance to its Euclidean distance is calculated, and the mean of the ratios of all selected point pairs is calculated to obtain the geodesic convexity index. Multiplying the volume of the three-dimensional convex hull formed by the dynamically adjusted set of boundary points by the geodesic convexity index, the resulting compressed geometry is the dynamic topological kernel of the healthy domain.

[0035] Starting from the dynamic topology kernel of the health domain, the shortest geodesic path from the test point to the dynamic topology kernel of the health domain is calculated, and then the tangent vector of the shortest geodesic path is extracted to generate a covariant curvature distortion vector. Specifically, this includes: taking the point on the surface of the dynamic topology kernel of the health domain that is closest to the test point as the starting point; treating all vertices of the dynamic topology kernel of the health domain (the vertices here are the corner points of the geometry) and the test point as nodes in a network; finding the 5 other nodes that are spatially closest to each node and connecting them with straight lines to form network lines, with the weight of each line being the straight-line distance between the two nodes; The shortest path search algorithm is used to find the path with the smallest sum of weights between the starting point and the test point. This path is the shortest geodesic path. Calculate the distribution characteristics of all healthy sample points in the three-dimensional feature space to obtain a 3x3 covariance matrix; calculate the inverse square root of the covariance matrix to obtain the whitening transformation matrix; multiply the three-dimensional coordinates of each point on the shortest geodesic path by this whitening transformation matrix to perform whitening transformation; calculate the coordinates of the last two consecutive points closest to the test point on the shortest geodesic path after whitening transformation, subtract the coordinates of the previous point from the coordinates of the next point to obtain a vector; normalize this vector to a unit vector to obtain the unit tangent vector. Calculate the local average normal vector of the healthy domain dynamic topological kernel surface after whitening transformation at the starting point, and normalize it to a unit vector as the local reference normal; calculate the cosine value of the angle between the unit tangent vector and the local reference normal. For the last three points on the shortest geodesic path after whitening transformation, determine a unique circle, calculate the reciprocal of the radius of curvature of this circle, and obtain the curvature. The cosine value and curvature are combined in sequence into a vector containing two components, which is the covariant curvature distortion vector.

[0036] In one embodiment, test points are constructed based on the time-varying decay rate, hysteresis energy loss ratio, and recovery trajectory stability coefficient. The geometric deviation vector between the test points and the baseline healthy reference area is calculated to obtain the tangent space projection length and normal penetration depth. The method also includes: The covariant curvature distortion vector is projected onto the tangent space at the dynamic topological kernel of the healthy domain, and the projection modulus is corrected using the defect topological potential energy scalar to obtain the projection length of the tangent space. Specifically, this includes: projecting the covariant curvature distortion vector onto the tangent space at the dynamic topological kernel of the healthy domain, and constructing two orthogonal unit basis vectors of the tangent space based on the local reference normal. The two components of the covariant curvature distortion vector are used as coefficients and linearly combined with the two orthogonal unit basis vectors to obtain its three-dimensional vector in the three-dimensional tangent space. Specifically, the two orthogonal unit basis vectors are denoted as the first basis vector U (Ux, Uy, Uz) and the second basis vector V (Vx, Vy, Vz). The two components of the covariant curvature distortion vector are respectively used as the first component coefficient a and the second component coefficient b; the X-axis coordinate component of the three-dimensional vector is a multiplied by Ux + b multiplied by Vx; the Y-axis coordinate component of the three-dimensional vector is a multiplied by Uy + b multiplied by Vy; the Z-axis coordinate component of the three-dimensional vector is a multiplied by Uz + b multiplied by Vz. Calculate the magnitude of the three-dimensional vector as the original projection magnitude; multiply the original projection magnitude by the natural logarithm of the defect topological potential energy scalar to obtain the corrected magnitude; calculate the hyperbolic tangent function value of the corrected magnitude, and then normalize the calculation result to the 0-1 interval to obtain the tangent space projection length.

[0037] The implicit curvature penetration potential is calculated based on the covariant curvature distortion vector, the defect topological potential scalar, and the healthy domain dynamic topological kernel. After mapping the implicit curvature penetration potential, the normal penetration depth is generated. Specifically, the curvature of the covariant curvature distortion vector is multiplied by the defect topological potential scalar, and then divided by the volume of the healthy domain dynamic topological kernel to obtain the implicit curvature penetration potential. Calculate the arithmetic square root of the absolute value of the latent curvature penetration potential, then calculate the arctangent function value of the square root value, multiply the arctangent function value by the cube root of the dynamic topological kernel volume of the healthy domain, and normalize the calculation result to the 0-1 interval to obtain the normal penetration depth.

[0038] Three-dimensional test points are constructed by using time-varying decay rate, hysteresis energy loss ratio, and recovery trajectory stability coefficient. Based on the defect topological potential energy scalar, the benchmark healthy reference area is dynamically adjusted to generate a dynamic topological kernel of the healthy domain, thereby adapting to the real distribution of healthy samples. The covariant curvature distortion vector is extracted by the shortest geodesic path. After tangent space projection and normal penetration depth calculation, noise interference introduced by crosstalk is effectively removed, accurately distinguishing the characteristic offset caused by crosstalk from the degradation of real components, reducing the false judgment rate, improving the accuracy of defect identification and positioning precision, and adapting to the testing requirements of ultra-high resolution printheads.

[0039] In one embodiment, the calculation process of the baseline health reference area includes: Multiple healthy target components are obtained and used as a sample set. The sample set is subjected to the same tests from step S1 to step S3 to obtain the standard time-varying decay rate, recovery trajectory stability coefficient and hysteresis energy loss ratio. The baseline healthy reference region is obtained by calculating the standard time-varying decay rate, recovery trajectory stability coefficient, and hysteresis energy loss ratio. Specifically, this involves: using the standard time-varying decay rate, recovery trajectory stability coefficient, and hysteresis energy loss ratio as a healthy reference point in three-dimensional space; calculating the Euclidean distance between all pairwise healthy reference points to obtain a complete distance set; calculating the median of the distance set and using this median value as the baseline distance; removing isolated points whose distances to all other points are greater than 1.5 times the baseline distance; and calculating the three-dimensional convex hull of all remaining healthy reference points as vertices. The spatial region enclosed by this convex hull is the baseline healthy reference region.

[0040] In one embodiment, the tangent space projection length and normal penetration depth are jointly determined to obtain a defect identification and location instruction, including: The sample set is subjected to the same tests from step S1 to step S4 to obtain the standard tangent space projection length and normal penetration depth. The standard tangent space projection length and normal penetration depth are calculated to obtain the first threshold and the second threshold. Specifically, the calculation includes: calculating the median of all standard tangent space projection lengths, then calculating the absolute value of the deviation of all tangent space projection lengths from the median, then calculating the median of these absolute values, which is denoted as the absolute median difference. The median plus 1.5 times the absolute median difference is the first threshold. Calculate the median of all standard normal penetration depths, then calculate the absolute value of the deviation of all normal penetration depths from the median, and then calculate the median of these absolute values, which is denoted as the absolute median difference. The median plus 1.5 times the absolute median difference is the second threshold.

[0041] The tangent space projection length and normal penetration depth are compared with the first threshold and the second threshold to obtain the defect identification and location instructions, which specifically include: When the tangent space projection length is less than or equal to the first threshold and the normal penetration depth is less than or equal to the second threshold, the target element is considered healthy and no command is issued. When the tangent space projection length is greater than the first threshold and the normal penetration depth is less than or equal to the second threshold, it is determined that the target element is degraded, and the first bad pixel instruction is issued. When the normal penetration depth is greater than the second threshold and the tangent space projection length is less than or equal to the first threshold, it is determined that the target component has poor contact, and a second bad point command is issued. When the tangent space projection length > the first threshold and the normal penetration depth > the second threshold, the two values ​​are further compared: If the tangent space projection length is greater than or equal to the normal penetration depth, it is determined to be an open circuit, and a third bad point instruction is issued. If the tangent space projection length is less than the normal penetration depth, it is determined to be a short circuit, and a fourth bad point instruction is issued. This allows the system to identify the type of defect in the target component and issue different commands. When a defect command is issued, the target component is the identified defect.

[0042] By deriving the median and absolute median difference of the tangent space projection length and normal penetration depth from the sample set, a first threshold and a second threshold are set to adapt to the real distribution of healthy samples and avoid the limitations of fixed thresholds. Based on the collaborative judgment logic of the two, it can accurately distinguish between healthy, degraded, poor contact, open circuit, short circuit and other states, avoid misjudging the characteristic deviation caused by crosstalk as component degradation, realize fine classification of defect types, reduce the misjudgment rate, improve positioning accuracy, adapt to the testing needs of ultra-high resolution and miniaturized thermal printheads, and enhance the stability and reliability of testing.

[0043] In one embodiment, a thermal printhead defect type identification and location system is applied to the aforementioned thermal printhead defect type identification and location method, including: The pulse test unit is used to apply two stress pulses to the target element of the thermal printhead and acquire multidimensional data in real time during the application of the two stress pulses. The target element is a single heating point of the printhead plate in the thermal printhead. The data analysis unit is used to calculate the stress response voltage, stress response current and two mutual interference voltages in the preprocessed multidimensional data, obtain the time-varying decay rate of the target element impedance during the first stress pulse application, and analyze the power parameters in the preprocessed multidimensional data to obtain the hysteresis energy loss ratio. The data calculation unit is used to construct the recovery trajectory curve based on the recovery voltage and recovery current in the preprocessed multidimensional data, and to analyze the recovery trajectory curve to obtain the recovery trajectory stability coefficient. The deviation analysis unit is used to construct test points based on the time-varying decay rate, hysteresis energy loss ratio and recovery trajectory stability coefficient, and to calculate the geometric deviation vector between the test points and the benchmark healthy reference area, thereby obtaining the tangent space projection length and normal penetration depth. The identification and positioning unit is used to collaboratively determine the tangent spatial projection length and normal penetration depth to obtain bad spot identification and positioning instructions.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for identifying and locating defective pixel types in a thermal printhead, characterized in that, include: Step S1: Apply two stress pulses to the target element of the thermal printhead and acquire multidimensional data in real time during the application of the two stress pulses. The target element is a single heating point of the printhead plate in the thermal printhead. The multidimensional data includes: during the application of the first stress pulse, the stress response voltage across the target element and the stress response current flowing through it, as well as the mutual interference voltage between two adjacent target elements; Mutual interference voltage is the transient interference voltage induced on adjacent components through parasitic capacitance when a stress pulse is applied to the target component. Furthermore, the recovery voltage and recovery current during the recovery period after the second stress pulse ends are acquired synchronously and in real time, and the instantaneous power during the application of the first and second stress pulses is acquired in real time, respectively. Step S2: Calculate the stress response voltage, stress response current, and two mutual interference voltages in the preprocessed multidimensional data to obtain the time-varying decay rate of the target element impedance during the first stress pulse application period, and analyze the power parameters in the preprocessed multidimensional data to obtain the hysteresis energy loss ratio. Step S3: Construct a recovery trajectory curve based on the recovery voltage and recovery current in the preprocessed multidimensional data, and analyze the recovery trajectory curve to obtain the recovery trajectory stability coefficient; Step S4: Construct test points based on time-varying decay rate, hysteresis energy loss ratio and recovery trajectory stability coefficient, and calculate the geometric deviation vector between the test points and the benchmark healthy reference area to obtain the tangent space projection length and normal penetration depth. Step S5: Perform a joint judgment on the tangent space projection length and normal penetration depth to obtain the bad spot identification and location command.

2. The method for identifying and locating defective pixels in a thermal printhead according to claim 1, characterized in that, The order in which stress pulses are applied includes: Ten seconds after the first stress pulse ends, a second stress pulse is applied. Furthermore, the voltage amplitude of the stress pulse is higher than the rated operating voltage of the target component.

3. The method for identifying and locating defective pixel types in a thermal printhead according to claim 1, characterized in that, The stress response voltage, stress response current, and two mutual interference voltages in the preprocessed multidimensional data are calculated to obtain the time-varying decay rate of the target element impedance during the application of the first stress pulse. Furthermore, the power parameters in the preprocessed multidimensional data are analyzed to obtain the hysteresis energy loss ratio, including: The field gradient divergence is obtained by analyzing the instantaneous changes of stress response voltage, stress response current, and mutual disturbance voltage in the target element and its adjacent elements in multidimensional data. Specifically, during the first stress pulse, for each adjacent sampling point, the instantaneous changes of voltage and current are calculated, and the two are divided to obtain the dynamic differential conductance. At the same time, the mutual disturbance voltage on the adjacent elements is divided by the stress response current of the target element to obtain two virtual potentials. Taking the target element as the center, the dynamic differential conductance and the two virtual potentials are regarded as field nodes on a two-dimensional plane. The spatial difference between the central node and the adjacent nodes is calculated and convolved with the dynamic differential conductance. The convolution result is then spatiotemporally averaged to obtain the field gradient divergence. The intrinsic instability index is obtained by decomposing the stress response voltage and stress response current in the multidimensional data. The time-varying decay rate is obtained by fusing the field gradient divergence and the intrinsic instability index. Based on two instantaneous powers in the preprocessed multidimensional data, the peak values ​​of the instantaneous entropy productivity of the two stress pulses are analyzed to obtain the entropy productivity mutation degree. Specifically, this includes: partitioning the instantaneous power sequence of each pulse application process and calculating the probability value of each interval; simultaneously filtering the probability values ​​of each interval and calculating the instantaneous entropy productivity; traversing all sampling points to obtain the instantaneous entropy productivity curves of the two stress pulses, and calculating the entropy productivity mutation degree from the instantaneous entropy productivity curves. By analyzing the offset and hysteresis of the two instantaneous powers, the path offset margin is obtained; The entropy production mutation degree and path offset margin are calculated together to generate the hysteresis energy loss ratio.

4. The method for identifying and locating defective pixel types in a thermal printhead according to claim 1, characterized in that, A recovery trajectory curve is constructed based on the recovery voltage and recovery current from the preprocessed multidimensional data. The recovery trajectory curve is then analyzed to obtain the recovery trajectory stability coefficient, including: The time-series data of recovery voltage and recovery current are mapped to a recovery trajectory curve. The integral of the curl of adjacent state points on the time axis is calculated to obtain the convergence heterogeneity. The neighborhood of the endpoint of the recovered trajectory curve is regarded as a transient attractor. The change curve of the transient attractor is calculated to obtain the topological robustness index. The convergence heterogeneity and topological robustness index are calculated to obtain the stability coefficient of the recovered trajectory.

5. The method for identifying and locating defective pixels in a thermal printhead according to claim 4, characterized in that, Test points are constructed based on the time-varying decay rate, hysteresis energy loss ratio, and recovery trajectory stability coefficient. The geometric deviation vector between the test points and the baseline healthy reference area is calculated to obtain the tangent space projection length and normal penetration depth, including: The time-varying decay rate, hysteresis energy loss ratio, and recovery trajectory stability coefficient are used as test points, and analyzed with the baseline health reference area to generate a defect topological potential energy scalar. Based on the scalar of defect topological potential energy, the boundary of the benchmark healthy reference region is dynamically adjusted to generate a dynamic topological kernel for the healthy domain. Specifically, this includes: removing all healthy reference points in the benchmark healthy reference region to obtain a dynamically adjusted set of boundary points; randomly selecting two pairs of non-adjacent points in the set of boundary points for calculation to obtain the geodesic convexity index; and then multiplying the volume of the three-dimensional convex hull formed by the set of boundary points by the geodesic convexity index to obtain the compressed geometry, which is the dynamic topological kernel for the healthy domain. Starting from the dynamic topology kernel of the healthy domain, the shortest geodesic path from the test point to the dynamic topology kernel of the healthy domain is calculated, and then the tangent vector of the shortest geodesic path is extracted to generate the covariant curvature distortion vector.

6. The method for identifying and locating defective pixel types in a thermal printhead according to claim 5, characterized in that, Test points are constructed based on the time-varying decay rate, hysteresis energy loss ratio, and recovery trajectory stability coefficient. The geometric deviation vector between the test points and the baseline healthy reference area is calculated to obtain the tangent space projection length and normal penetration depth. This also includes: The covariant curvature distortion vector is projected onto the tangent space at the dynamic topological kernel of the healthy domain, and the projection modulus is corrected using the defect topological potential energy scalar to obtain the projection length of the tangent space. The latent curvature penetration potential is calculated based on the covariant curvature distortion vector, the defect topological potential energy scalar, and the dynamic topological kernel of the healthy domain. After mapping the latent curvature penetration potential, the normal penetration depth is generated.

7. The method for identifying and locating defective pixels in a thermal printhead according to claim 5, characterized in that, The calculation process for the baseline health reference area includes: Multiple healthy target components are obtained and used as a sample set. The sample set is subjected to the same tests from step S1 to step S3 to obtain the standard time-varying decay rate, recovery trajectory stability coefficient and hysteresis energy loss ratio. The baseline health reference region is obtained by calculating the standard time-varying decay rate, recovery trajectory stability coefficient, and hysteresis energy loss ratio.

8. The method for identifying and locating defective pixel types in a thermal printhead according to claim 6, characterized in that, By jointly judging the tangent space projection length and normal penetration depth, a defect identification and location instruction is obtained, including: The sample set is subjected to the same tests from step S1 to step S4 to obtain the standard tangent space projection length and normal penetration depth. The standard tangent space projection length and normal penetration depth are calculated to obtain the first threshold and the second threshold. By comparing the tangent space projection length and normal penetration depth with the first threshold and the second threshold, a bad pixel identification and location command is obtained.

9. A thermal printhead defect type identification and positioning system, applied to the thermal printhead defect type identification and positioning method according to any one of claims 1-8, characterized in that, include: The pulse test unit is used to apply two stress pulses to the target element of the thermal printhead and acquire multidimensional data in real time during the application of the two stress pulses. The target element is a single heating point of the printhead plate in the thermal printhead. The data analysis unit is used to calculate the stress response voltage, stress response current and two mutual interference voltages in the preprocessed multidimensional data, obtain the time-varying decay rate of the target element impedance during the first stress pulse application, and analyze the power parameters in the preprocessed multidimensional data to obtain the hysteresis energy loss ratio. The data calculation unit is used to construct the recovery trajectory curve based on the recovery voltage and recovery current in the preprocessed multidimensional data, and to analyze the recovery trajectory curve to obtain the recovery trajectory stability coefficient. The deviation analysis unit is used to construct test points based on the time-varying decay rate, hysteresis energy loss ratio and recovery trajectory stability coefficient, and to calculate the geometric deviation vector between the test points and the benchmark healthy reference area, thereby obtaining the tangent space projection length and normal penetration depth. The identification and positioning unit is used to collaboratively determine the tangent spatial projection length and normal penetration depth to obtain bad spot identification and positioning instructions.

Images