A dlts multi-time window parameter extraction system and method

Abstract

The application provides a DLTS multi-time window parameter extraction system and method, which comprises a temperature control module, a transient capacitance collection module, a multi-time window control module and a data processing module. The temperature control module is used for providing a temperature scanning environment and triggering signal collection after the temperature meets a stable determination condition. The transient capacitance collection module is used for collecting transient capacitance signals of a semiconductor device under test. The multi-time window control module is used for sampling or integrating the transient capacitance signals at the same temperature point according to different time window parameters in multiple groups, to generate multiple groups of DLTS signal values. The data processing module is used for jointly fitting the multiple groups of DLTS signal values, to obtain an emissivity parameter, and extracting a defect activation energy and a capture cross section based on the emissivity parameters at multiple temperature points, to improve the stability, reliability and repeatability of the calculation of the defect activation energy and the capture cross section.

Description

Technical Field

[0001] This invention relates to the field of semiconductor device and material testing technology, and in particular to a DLTS multi-time window parameter extraction system and method. Background Technology

[0002] With the continuous development of semiconductor device technology, the impact of deep-level defects on device leakage current, lifetime, and reliability is becoming increasingly prominent. Deep-level transient spectroscopy (DLTS), as a commonly used defect characterization method, can obtain the emissivity parameters of defects by analyzing the transient capacitance response of the device after bias pulse excitation, and further extract key physical parameters such as defect activation energy and trapping cross section. Therefore, it is widely used in the research of power devices, wide-bandgap semiconductors, and new materials.

[0003] However, in actual engineering testing, conventional DLTS measurement and parameter extraction still face the following problems: First, there is a lack of unified quantitative criteria for temperature stability control. Existing testing procedures typically start signal acquisition once the temperature control system reaches the target temperature. However, due to the thermal inertia of the temperature control system and environmental disturbances, the device temperature may still exhibit a certain rate of change near the target point. The carrier thermal emission process is highly sensitive to temperature; even small temperature fluctuations can introduce deviations in the emissivity parameter, thereby affecting the stability and repeatability of subsequent fitting results.

[0004] Secondly, the transient capacitance signal processing strategy is relatively simple. Traditional DLTS methods often use a fixed time window to sample or integrate the transient capacitance signal to obtain the corresponding DLTS signal value. This method is quite sensitive to noise and fluctuations in test conditions. When the defect response time constant distribution is wide or multiple defect responses are superimposed in similar temperature ranges, it can easily lead to increased signal dispersion, thus affecting the reliability of emissivity parameter extraction.

[0005] Third, the fitting process lacks robustness. Due to the cumulative effects of factors such as temperature fluctuations, noise interference, and the superposition of defect responses, the emissivity parameters obtained by traditional methods have large dispersion, resulting in poor stability of the fitting process based on this data. This leads to significant errors in the calculation of defect activation energy and trapping cross section. Summary of the Invention

[0006] This invention provides a DLTS multi-time-window parameter extraction system and method, aiming to solve the problems in existing DLTS measurement technology, such as the lack of temperature stability criteria, the large dispersion of emissivity parameters caused by the single time-window processing method, and the insufficient fitting stability under the superposition of defect responses, thereby improving the stability, reliability and repeatability of defect activation energy and capture section calculation.

[0007] This invention discloses a DLTS multi-time window parameter extraction system, the system comprising: The temperature control module is configured to provide a temperature scanning environment for the semiconductor device under test. The transient capacitance acquisition module is configured to acquire the transient capacitance signal of the semiconductor device under test during the bias period and after the bias pulse is removed after the temperature stability judgment condition is met at the target temperature point. The multi-time-window control module is configured to perform time-window sampling or integration processing on the transient capacitance signal collected under the same temperature conditions according to multiple sets of different time-window parameters, and generate corresponding multiple sets of DLTS signal values. The data processing module is configured to perform joint fitting processing on multiple sets of DLTS signal values ​​to obtain emissivity parameters at corresponding temperatures; and to perform fitting processing on the emissivity parameters obtained at multiple different temperatures to extract defect activation energy and capture cross sections.

[0008] Furthermore, it also includes a bias pulse excitation module, which is configured to apply a preset bias pulse to the semiconductor device under test.

[0009] Furthermore, the transient capacitance acquisition module includes an impedance analyzer or a capacitance measurement unit; The impedance analyzer or the capacitance measurement unit is used to acquire the transient capacitance signal of the semiconductor device under test during the bias period and after the bias pulse is removed, after the temperature stability judgment condition is met at the target temperature point.

[0010] Furthermore, the temperature control module also includes: The temperature detection unit is configured to detect real-time temperature change data of the semiconductor device under test; The temperature calculation unit is configured to calculate the rate of temperature change of the semiconductor device under test at the target temperature point based on the real-time temperature change data. The judgment unit is configured to compare the temperature change rate with a preset threshold, and when the comparison result meets the temperature stability judgment condition, control the transient capacitance acquisition module to acquire the transient capacitance signal at the corresponding target temperature point.

[0011] Furthermore, the data processing module includes: The joint fitting function building unit is configured to construct a joint fitting function that integrates the results of multiple time window processing based on the exponential decay physical model describing the transient response of the capacitor. The emissivity parameter generation unit is configured to perform joint fitting on multiple sets of DLTS signal values ​​based on the joint fitting function to obtain the emissivity parameter at the corresponding target temperature point.

[0012] Furthermore, the data processing module also includes an anomaly removal unit; The anomaly removal unit is configured to remove or weight abnormal emissivity parameters in the emissivity parameters based on the fitting residuals generated after joint fitting or the uncertainty of the emissivity parameters.

[0013] Furthermore, the data processing module also includes a defect activation energy and a capture section extraction unit; The defect activation energy and trapping section extraction unit is configured to construct an Arrhenius relationship model between the emissivity parameter and temperature, and extract the defect activation energy and trapping section based on the Arrhenius relationship model.

[0014] On the other hand, the present invention also discloses a method for extracting multiple time windows parameters in DLTS, the method comprising: Provide a temperature scanning environment for the semiconductor device under test; After the temperature stability judgment condition is met at the target temperature point, the transient capacitance signal of the semiconductor device under test is collected during the bias period and after the bias pulse is removed. For the transient capacitance signal collected under the same temperature conditions, time window sampling or integration processing is performed according to multiple sets of different time window parameters to generate multiple sets of corresponding DLTS signal values; Multiple sets of DLTS signal values ​​are jointly fitted to obtain emissivity parameters at corresponding temperatures; and the emissivity parameters obtained at multiple different temperatures are fitted to extract defect activation energy and capture cross-section.

[0015] Furthermore, the emissivity parameters obtained at multiple different temperatures are fitted to extract the defect activation energy and trapping cross section, including: Based on the fitting residuals or the uncertainty of the emissivity parameters generated after joint fitting, abnormal emissivity parameters in the emissivity parameters are removed or weighted, and an Arrhenius relationship model between the emissivity parameters and temperature is constructed. Based on the Arrhenius relationship model, the defect activation energy and the trapping cross section are extracted.

[0016] Compared with the prior art, the present invention has at least the following technical effects: This invention introduces a temperature change rate determination mechanism, allowing transient capacitance signal acquisition only after the temperature stabilizes. This effectively reduces the impact of temperature drift on emissivity parameter extraction, improving the repeatability and reliability of measurement results. The use of a multi-time-window joint processing strategy allows for the acquisition of multiple sets of DLTS signal values ​​at the same temperature point, helping to reduce the impact of superimposed responses from different defects on emissivity parameter extraction and providing more sufficient data support for subsequent joint fitting. Furthermore, this invention improves the stability, reliability, and repeatability of defect activation energy and capture cross-section calculations by performing overall fitting of emissivity parameters at multiple temperature points.

[0017] Furthermore, the fitting steps disclosed in this invention, combined with outlier removal or weighted processing, improve the stability of Arrhenius fitting, and further enhance the stability, reliability, and repeatability of defect activation energy and capture cross-section calculations.

[0018] Furthermore, the bias pulse excitation module disclosed in this invention can output a bias signal with a preset pulse width, pulse amplitude and repetition frequency. By rapidly switching between forward bias and reverse bias, the deep level defects of the semiconductor device under test can complete the cyclic process of carrier filling and release, providing the original data basis for subsequent multi-time window DLTS analysis.

[0019] Furthermore, this invention is applicable to engineering application scenarios within the conventional DLTS testing framework, and can improve the stability, reliability, and repeatability of defect activation energy and capture section calculations without changing the existing measurement hardware structure. Attached Figure Description

[0020] Figure 1 This is a simplified schematic diagram of the DLTS multi-time window parameter extraction system in Embodiment 1 of the present invention; Figure 2 This is a simplified flowchart illustrating the DLTS multi-time window parameter extraction method in Embodiment 2 of the present invention. Detailed Implementation

[0021] The following description, or illustration, with reference to the schematic diagrams, outlines a DLTS multi-time window parameter extraction system and method according to the present invention, illustrating preferred embodiments of the invention. It should be understood that those skilled in the art can modify the invention described herein while still achieving its advantageous effects. Therefore, the following description should be understood as being of general knowledge to those skilled in the art and is not intended to limit the invention.

[0022] The invention is described more specifically by way of example in the following paragraphs with reference to the accompanying drawings. The advantages and features of the invention will become clearer from the following description. It should be noted that the drawings are in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the invention.

[0023] Example 1 Please refer to Figure 1 This embodiment discloses a DLTS multi-time window parameter extraction system, the system comprising: The temperature control module is configured to provide a temperature scanning environment for the semiconductor device under test. The transient capacitance acquisition module is configured to acquire the transient capacitance signal of the semiconductor device under test during the bias period and after the bias pulse is removed after the temperature stability judgment condition is met at the target temperature point. The multi-time-window control module is configured to perform time-window sampling or integration processing on the transient capacitance signal collected under the same temperature conditions according to multiple sets of different time-window parameters, and generate corresponding multiple sets of DLTS signal values. The data processing module is configured to perform joint fitting processing on multiple sets of DLTS signal values ​​to obtain emissivity parameters at corresponding temperatures; and to perform fitting processing on the emissivity parameters obtained at multiple different temperatures to extract defect activation energy and capture cross sections.

[0024] In this embodiment, the transient capacitance acquisition module first acquires the transient capacitance signal after the temperature meets the stability criteria, thereby reducing the interference of temperature drift on emissivity calculation. Then, a multi-time window control module is used to process the acquired transient capacitance signal according to multiple sets of different time window parameters to obtain multiple sets of DLTS signal values, providing richer data support for subsequent joint fitting. Subsequently, the data processing module performs joint fitting processing on the multiple sets of DLTS signal values, which helps to reduce the impact of the superposition of different defect responses on emissivity parameter extraction. Finally, by performing overall fitting of emissivity parameters under multiple different temperature conditions, the stability, reliability, and repeatability of defect activation energy and capture section calculation are improved.

[0025] In other words, the temperature stability condition mentioned in this embodiment means that the absolute value of the temperature change rate of the tested semiconductor device is less than a set threshold and remains so for a certain period of time.

[0026] Furthermore, since the present invention employs a data processing strategy of joint fitting of multiple time windows, the impact of environmental disturbances on the results can be reduced, enabling the system and method provided by the present invention to be applicable to more complex test environment conditions, including complex test scenarios such as relatively low temperature control accuracy, large environmental interference, or multiple overlapping defect energy levels in the semiconductor device under test, thereby improving the applicability of the system under complex test conditions.

[0027] In this embodiment, the transient capacitance acquisition module can acquire transient capacitance signals from the semiconductor device under test. The transient capacitance signal is the raw transient response signal generated by the semiconductor device under test under bias excitation. After multi-time-window sampling or integration processing of the transient capacitance signal, a corresponding DLTS signal value can be generated for subsequent emissivity parameter extraction. Based on multi-time-window processing and joint fitting analysis of the transient capacitance signal, defect activation energy and trapping cross-section can be further extracted.

[0028] In this embodiment, the system further includes a bias pulse excitation module, configured to apply a preset bias pulse to the semiconductor device under test.

[0029] In this embodiment, the bias pulse excitation module can output a bias signal with a preset pulse width, pulse amplitude and repetition frequency. By rapidly switching between forward bias and reverse bias, the deep level defects of the semiconductor device under test can complete the cycle of carrier filling and release, providing the original data basis for subsequent multi-time window DLTS analysis.

[0030] In one specific example, the transient capacitance acquisition module includes an impedance analyzer or a capacitance measurement unit.

[0031] The impedance analyzer or the capacitance measurement unit is used to acquire the transient capacitance signal of the semiconductor device under test during the bias period and after the bias pulse is removed, after the temperature stability judgment condition is met at the target temperature point.

[0032] In a specific example, the temperature control module includes: a temperature detection unit, a temperature calculation unit, and a judgment unit.

[0033] The temperature detection unit is configured to detect real-time temperature change data of the semiconductor device under test. The temperature calculation unit is configured to calculate the temperature change rate of the semiconductor device under test at a target temperature point based on the real-time temperature change data. The judgment unit is configured to compare the temperature change rate with a preset threshold, and when the comparison result meets the temperature stability judgment condition, control the transient capacitance acquisition module to acquire the transient capacitance signal at the corresponding target temperature point.

[0034] In a specific example, the temperature change rate can be calculated as follows: when the temperature control module detects that the temperature of the semiconductor device under test is close to the target temperature, it continuously collects temperature data T(t) at fixed time intervals Δt, calculates the temperature difference ΔT between adjacent sampling points, and the temperature change rate r = ΔT / Δt. For example, the sampling interval Δt can be set to 1 second. If the current temperature is T1 and the previous temperature was T0, then the temperature change rate r = (T1-T0) / 1s. To improve the stability of the judgment, a sliding window averaging method can also be used to average the temperature change rate over multiple consecutive sampling periods before comparing it with a threshold.

[0035] In this embodiment, the threshold for the sustained stable time of the temperature change rate can be set according to specific testing requirements, such as adjusting it according to the material type, the response characteristics of the temperature control system, or the measurement accuracy requirements, and is not specifically limited here.

[0036] In a specific example, the threshold for the rate of temperature change can be set to 0.1 K / s, meaning that when the absolute value of the rate of temperature change is less than 0.1 Kelvin per second, the temperature is considered to be stable. For test scenarios with high temperature sensitivity, the threshold for the rate of temperature change can be set to 0.05 K / s or less. For scenarios with high test efficiency requirements but relatively relaxed accuracy requirements, the threshold for the rate of temperature change can be set to 0.2 K / s.

[0037] In another specific example, the sustained stabilization time threshold of the temperature change rate can be set to 10 seconds, that is, the absolute value of the temperature change rate must be less than the set threshold for at least 10 seconds before the current temperature is determined to meet the stabilization condition; for test systems with large thermal inertia, the sustained stabilization time threshold can be set to 30 seconds or longer; for low-temperature test systems with fast response speed, the sustained stabilization time threshold can be set to 5 seconds.

[0038] In this embodiment, signal acquisition is initiated only after the absolute value of the temperature change rate is consistently less than a set threshold, allowing DLTS measurements to be performed on the semiconductor device under test in a thermal equilibrium state. This is because the thermal emission process of charge carriers is highly sensitive to temperature; the emissivity is exponentially related to temperature, and even small temperature changes can cause significant deviations in emissivity. If transient capacitance signals are acquired before the temperature has fully stabilized, the internal temperature gradient and thermal transient effects will directly affect the shape of the transient capacitance curve, leading to distortion in the emissivity extraction results. Therefore, entering the transient capacitance signal acquisition stage only after the temperature meets the stability criterion effectively eliminates the interference of temperature drift on the measurement results and avoids measurement errors introduced by acquiring transient capacitance signals before the temperature has fully stabilized.

[0039] In this embodiment, the data processing module includes: The joint fitting function construction unit is configured to construct a joint fitting function that integrates the results of multiple time window processing steps, based on an exponentially decaying physical model describing the transient response of the capacitor. The exponentially decaying model describes the time dependence of the transient capacitance signal.

[0040] The emissivity parameter generation unit is configured to perform joint fitting on multiple sets of DLTS signal values ​​based on the joint fitting function to obtain the emissivity parameter at the corresponding target temperature point.

[0041] In this embodiment, the multiple time windows can be multiple time windows that are set discretely, that is, several sets of specific time window parameters determined based on test experience or theoretical analysis; or they can be a group of time windows distributed according to a predetermined rule, such as being set according to a logarithmic scale distribution or a linear scale distribution.

[0042] Those skilled in the art can select the specific number and distribution of multiple time windows based on the defect time constant range of the material under test, the sampling capability of the test system, and the parameter analysis requirements; no specific restrictions are imposed here.

[0043] Furthermore, the advantages of using the exponential decay model are as follows: the model is directly based on the physical process of carriers being released from deep-level defects by thermal excitation, and has a clear physical meaning; in addition, the exponential decay function is simple in form, which is convenient for mathematical processing and numerical optimization; by fitting multiple time windows together, the information of different time windows can be fully utilized, the reliability of emissivity extraction can be improved, and the influence of noise on the fitting effect can be reduced.

[0044] Of course, those skilled in the art can choose other suitable transient response models based on the characteristics of the actual test scenario and the type of defect, and no specific restrictions are imposed here.

[0045] In a specific example, the number of time window parameters corresponding to the same transient capacitance signal can be set to 5 groups, distributed in a logarithmic scale. The specific time window parameters are: (t1=0.5ms, t2=1ms), (t1=1ms, t2=2ms), (t1=2ms, t2=4ms), (t1=5ms, t2=10ms), and (t1=10ms, t2=20ms). This logarithmic distribution allows the time window to cover a wide time constant range from 0.5ms to 20ms, which is suitable for simultaneously detecting defects in both fast and slow emission processes.

[0046] In another specific example, the number of time window parameters corresponding to the same transient capacitance signal is set to 8 groups, and a linear scale distribution is adopted. The time window parameters increase from (t1=1ms, t2=2ms) in 1ms increments to (t1=8ms, t2=9ms). This distribution method is suitable for scenarios that require fine analysis of a specific time constant interval.

[0047] In this embodiment, by using multiple sets of different time window parameters to process the same transient capacitance signal, the system can obtain richer signal information from a single transient capacitance acquisition, providing a sufficient data foundation for subsequent joint fitting processing. Compared to the traditional single-time-window method, which can only acquire one signal value, the multi-time-window method significantly increases data redundancy, which is beneficial for suppressing the influence of random noise in subsequent fitting processes and improving the stability of defect activation energy and capture cross-section calculations.

[0048] Furthermore, in this embodiment, the data processing module also includes an anomaly removal unit.

[0049] The anomaly removal unit is configured to remove or weight abnormal emissivity parameters in the emissivity parameters based on the fitting residuals generated after joint fitting or the uncertainty of the emissivity parameters.

[0050] In this embodiment, the above-mentioned anomaly removal unit removes or weights abnormal emissivity parameters based on the uncertainty or fitting residual of the emissivity parameters. This reduces emissivity deviation caused by temperature fluctuations or noise, improves the consistency and reliability of emissivity parameters at different temperatures, provides more accurate input data for subsequent Arrhenius relation model fitting, and thus improves the stability of defect activation energy and capture section extraction.

[0051] Here, the abnormal emissivity parameter refers to the emissivity parameter point that significantly deviates from the expected trend of the Arrhenius relation model. In this embodiment, the benefits of removing or weighting the abnormal emissivity parameter are: it can reduce the impact of individual abnormal data points caused by factors such as test noise, temperature fluctuations, and interference from overlapping defects on the overall fitting result, improve the stability and reliability of the Arrhenius fitting, reduce the dispersion of the data, and thus obtain more reliable defect activation energies and capture cross sections.

[0052] In a specific example, the method for removing anomalous emissivity parameters is as follows: First, perform a preliminary Arrhenius fitting on the emissivity parameters of all temperature points to obtain a fitted straight line; then, calculate the residual from each data point to the fitted straight line; next, set a residual threshold; finally, after removing outliers, perform Arrhenius fitting again to obtain the final extraction defect activation energy and capture cross section.

[0053] In another specific example, the method for weighting the anomalous emissivity parameter is as follows: First, calculate the weight factor for each temperature point based on the uncertainty or fitting residual of the emissivity parameter; then, perform Arrhenius fitting using weighted least squares to reduce the contribution of anomalous points to the fitting result; finally, extract the defect activation energy and trapping cross section based on the weighted fitting result.

[0054] Furthermore, the data processing module also includes a defect activation energy and a capture section extraction unit.

[0055] The defect activation energy and trapping section extraction unit is configured to construct an Arrhenius relationship model between the emissivity parameter and temperature, and extract the defect activation energy and trapping section based on the Arrhenius relationship model.

[0056] In this embodiment, the Arrhenius relation model describes the relationship between carrier emissivity and temperature.

[0057] ; Where e(T) is the emissivity parameter, and T is the absolute temperature. Let Et be the defect activation energy, where Et is the trapping section. Boltzmann's constant, For thermal velocity, This represents the effective density of states.

[0058] Taking the logarithm of the above relationship yields: ; Therefore, it can be achieved by... The activation energy was obtained by linearly fitting the equation with 1 / T. and capture section .

[0059] In the specific examples above, the advantages of using the Arrhenius relationship model are as follows: the model is based on the fundamental principle of thermal excitation of charge carriers in solid-state physics and has a solid theoretical foundation; by converting the emissivity-temperature relationship into a linear form, it can be fitted using a linear regression method, which is simple to calculate and yields stable results; the Arrhenius diagram is a standard characterization method in DLTS analysis, which facilitates comparison with literature data and databases.

[0060] Example 2 Please refer to Figure 2 Based on the same inventive concept, this embodiment discloses a DLTS multi-time window parameter extraction method. The method is implemented using the DLTS multi-time window parameter extraction system disclosed in Embodiment 1. Specifically, the method includes: S1. Provide a temperature scanning environment for the semiconductor device under test; S2. After the temperature stability judgment condition is met at the target temperature point, the transient capacitance signal of the semiconductor device under test is collected during the bias period and after the bias pulse is removed. S3. For the transient capacitance signal collected under the same temperature conditions, perform time window sampling or integration processing according to multiple sets of different time window parameters to generate multiple sets of corresponding DLTS signal values; S4. Perform joint fitting processing on multiple sets of DLTS signal values ​​to obtain emissivity parameters at corresponding temperatures; and perform fitting processing on the emissivity parameters obtained at multiple different temperatures to extract defect activation energy and capture cross section.

[0061] In this embodiment, transient capacitance signals are first acquired after the stability criteria are met at the target temperature point, thereby reducing the interference of temperature drift on emissivity calculation. Then, multiple sets of different time window parameters are used to process the acquired transient capacitance signals to obtain multiple sets of DLTS signal values, providing richer data support for subsequent joint fitting. Subsequently, joint fitting of multiple sets of DLTS signal values ​​helps to reduce the impact of the superposition of different defect responses on emissivity parameter extraction. Finally, by performing overall fitting of emissivity parameters under multiple different temperature conditions, the stability, reliability, and repeatability of defect activation energy and capture section calculations are improved.

[0062] Furthermore, the emissivity parameters obtained at multiple different temperatures are fitted to extract the defect activation energy and trapping cross section. This includes: Based on the fitting residuals or the uncertainty of the emissivity parameters generated after joint fitting, abnormal emissivity parameters in the emissivity parameters are removed or weighted, and an Arrhenius relationship model between the emissivity parameters and temperature is constructed. Based on the Arrhenius relationship model, the defect activation energy and the trapping cross section are extracted.

[0063] In step S4, the benefits of removing or weighting the abnormal emissivity parameters are: it can reduce the impact of individual abnormal data points caused by factors such as test noise, temperature fluctuations, and interference from overlapping defects on the overall fitting results, improve the stability and reliability of Arrhenius fitting, reduce the dispersion of data, and thus obtain more reliable defect activation energies and capture cross sections.

[0064] In this embodiment, the above-mentioned DLTS multi-time window parameter extraction method and the DLTS multi-time window parameter extraction system disclosed in Embodiment 1 are based on the same technical design principle. Both can achieve the same technical purpose and the same technical effect. The effect that the system can achieve has been described in detail in Embodiment 1, so it will not be repeated here.

[0065] Those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A DLTS multi-time-window parameter extraction system, characterized in that, The system includes: The temperature control module is configured to provide a temperature scanning environment containing multiple target temperature points for the semiconductor device under test. The temperature control module includes: A temperature detection unit is configured to detect real-time temperature change data of the semiconductor device under test; The temperature calculation unit is configured to calculate the rate of temperature change of the semiconductor device under test at the target temperature point based on the real-time temperature change data. The judgment unit is configured to compare the temperature change rate with a preset threshold. When the absolute value of the temperature change rate is less than the preset threshold and continues for a preset time, the unit determines that the target temperature point meets the temperature stability judgment condition and controls the transient capacitance acquisition module to acquire the transient capacitance signal at the corresponding target temperature point. The transient capacitance acquisition module is configured to acquire the transient capacitance signal of the semiconductor device under test at the target temperature point after the temperature stability determination condition is met at the target temperature point, during the bias period and after the bias pulse is removed. The multi-time-window control module is configured to perform time-window sampling or integration processing on the transient capacitance signal collected at the same target temperature point according to multiple sets of different time-window parameters, and generate corresponding multiple sets of DLTS signal values. The data processing module is configured to perform joint fitting processing on multiple sets of DLTS signal values ​​to obtain emissivity parameters at corresponding temperatures; and to perform fitting processing on the emissivity parameters obtained at multiple different temperatures to extract defect activation energy and capture cross section; The data processing module includes: The joint fitting function building unit is configured to construct a joint fitting function that integrates the results of multiple time window processing based on the exponential decay physical model describing the transient response of the capacitor. The emissivity parameter generation unit is configured to perform joint fitting on multiple sets of DLTS signal values ​​based on the joint fitting function to obtain the emissivity parameter at the corresponding target temperature point. The anomaly removal unit is configured to remove or weight abnormal emissivity parameters in the emissivity parameters based on the fitting residuals generated after joint fitting or the uncertainty of the emissivity parameters. The defect activation energy and trapping section extraction unit is configured to construct an Arrhenius relationship model between the emissivity parameter and temperature, and extract the defect activation energy and trapping section based on the Arrhenius relationship model.

2. The DLTS multi-time-window parameter extraction system as described in claim 1, characterized in that, The system further includes a bias pulse excitation module, configured to apply a preset bias pulse to the semiconductor device under test.

3. The DLTS multi-time-window parameter extraction system as described in claim 1, characterized in that, The transient capacitance acquisition module includes an impedance analyzer or a capacitance measurement unit; The impedance analyzer or the capacitance measurement unit is used to acquire the transient capacitance signal of the semiconductor device under test during the bias period and after the bias pulse is removed, after the temperature stability judgment condition is met at the target temperature point.

4. A method for extracting parameters across multiple time windows in DLTS, characterized in that, The method is implemented using the DLTS multi-time-window parameter extraction system as described in any one of claims 1-3, and includes: Provides a temperature scanning environment containing multiple target temperature points for the semiconductor device under test; The real-time temperature change data of the semiconductor device under test is detected, and the temperature change rate of the semiconductor device under test at the target temperature point is calculated based on the real-time temperature change data. The temperature change rate is compared with a preset threshold. When the absolute value of the temperature change rate is less than the preset threshold and continues for a preset time, the target temperature point is determined to meet the temperature stability determination condition. After the temperature stability judgment condition is met at the target temperature point, the transient capacitance signal of the semiconductor device under test at that temperature point is collected during the bias period and after the bias pulse is removed. The transient capacitance signal collected at the same target temperature point is sampled or integrated according to multiple sets of different time window parameters to generate multiple sets of corresponding DLTS signal values. Based on the exponential decay physical model describing the transient response of a capacitor, a joint fitting function that integrates the results of multiple time window processing is constructed. Based on the joint fitting function, multiple sets of DLTS signal values ​​obtained by processing the same transient capacitance signal at the same target temperature point with multiple sets of different time window parameters are jointly fitted to obtain the emissivity parameter at the corresponding target temperature point. The emissivity parameters obtained at multiple different temperatures are fitted to extract the defect activation energy and the trapping cross section.

5. The DLTS multi-time-window parameter extraction method as described in claim 4, characterized in that, Fitting the emissivity parameters obtained at multiple different temperatures to extract the defect activation energy and trapping cross section includes: Based on the fitting residuals or the uncertainty of the emissivity parameters generated after joint fitting, abnormal emissivity parameters in the emissivity parameters are removed or weighted, and an Arrhenius relationship model between the emissivity parameters and temperature is constructed. Based on the Arrhenius relationship model, the defect activation energy and the trapping cross section are extracted.

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