A semiconductor defect test data resolvability determination and processing method and system

Abstract

The application provides a semiconductor defect test data resolvability determination and processing method and system, including: obtaining test data and corresponding test condition information; analyzing the test condition information, constructing a test condition parameter set; determining whether the test condition parameter set meets a preset resolution rule to determine whether the test data is resolvable; if it is determined that the test data is not resolvable or the resolution condition is insufficient, outputting condition gap information and generating a processing instruction based on the condition gap information; if it is determined that the test data meets the resolution condition, executing a data resolution process matched with the test condition parameter set to extract defect characterization parameters. The application improves the accuracy and applicability of semiconductor defect test data resolution, realizes a process closed loop of data acquisition, resolvability determination, processing instruction generation and defect characterization parameter extraction.

Description

Technical Field

[0001] This invention relates to the field of semiconductor device and material testing technology, and in particular to a method and system for determining and processing the resolvability of semiconductor defect test data. Background Technology

[0002] Deep-level transient spectroscopy (DLTS) and deep-level capacitance profiling (DLCP) are core technologies in semiconductor defect characterization and electrical testing, playing an irreplaceable role in semiconductor material development, device manufacturing, and reliability assessment. As semiconductor technology rapidly advances towards higher precision and reliability, the industry demands greater stability, accuracy, and engineering applicability of DLTS / DLCP test and analysis results. However, existing technologies still face numerous unresolved issues in practical applications. First, the existing parsing process lacks a pre-judgment mechanism for the legality of test conditions. Existing defect characterization parameter extraction algorithms generally assume that the input test data naturally meets the parsing conditions. Before formal parsing, the set of test condition parameters is not systematically evaluated. This leads to problems such as insufficient coverage of test conditions, insufficient sampling density, or incomparability of multiple sets of data. Even then, the parsing process still proceeds as usual, ultimately outputting distorted or unreliable defect characterization parameter extraction results.

[0003] Secondly, when the quality of test data is insufficient, the existing solution cannot clearly identify which rules are not met, nor can it provide a quantitative description of the gap, making it difficult for operators to determine the problem with the test data and thus unable to take targeted remedial measures.

[0004] Third, existing technologies lack the ability to automatically generate processing instructions when test data does not meet the parsing conditions. When there are gaps in the test conditions, the system cannot guide operators to complete the test conditions, nor can it automatically match or switch to an appropriate data parsing process based on the current actual set of test condition parameters. This results in subsequent parsing work relying entirely on manual experience and judgment, which is inefficient and prone to introducing human error. Summary of the Invention

[0005] This invention provides a method and system for determining and processing the resolvability of semiconductor defect test data, so as to improve the accuracy and applicability of semiconductor defect test data analysis and realize a closed-loop process of data acquisition, resolvability determination, processing instruction generation and defect characterization parameter extraction.

[0006] This invention provides a method for determining and processing the resolvability of semiconductor defect test data, the method comprising: Obtain test data and the corresponding test condition information; The test condition information is parsed to construct a set of test condition parameters; The preset parsing rules are invoked to match and judge the set of test condition parameters, and the parsability judgment result of the test data is output. If it is determined that the test data cannot be parsed or the parsing conditions are insufficient, output condition gap information and generate processing instructions based on the condition gap information; If the test data is determined to meet the parsing conditions, a data parsing process matching the test condition parameter set is executed to extract defect characterization parameters.

[0007] Furthermore, the test data includes at least one of capacitance signals and transient signals.

[0008] Furthermore, the test condition information includes at least one of the following: frequency information, bias voltage information, temperature information, AC excitation amplitude information, time window information, and sampling window information corresponding to the test data.

[0009] Furthermore, the set of test condition parameters includes at least one of the following: the frequency distribution of the electrical signal, the coverage of the test condition information, and the sampling density of the test data.

[0010] Furthermore, the step of invoking preset parsing rules to match and judge the set of test condition parameters and outputting the parsability determination result of the test data includes at least one of the following steps: Determine whether the test condition information corresponding to multiple sets of test data is comparable; Determine whether the data point sampling density of the test data reaches the preset resolution; Determine whether the coverage of the test condition information matches the extraction requirements of the defect characterization parameters.

[0011] Furthermore, the condition gap information includes at least one of the following: a rule item identifier that is not satisfied, a threshold or target interval corresponding to the rule item identifier that is not satisfied, and the parameter missing amount corresponding to the rule item identifier that is not satisfied.

[0012] Furthermore, the processing instructions include at least one of the following: Generate a set of supplementary test suggestion parameters corresponding to the test data, and perform supplementary tests based on the set of supplementary test suggestion parameters to complete the test condition information; Generate a data parsing process corresponding to the test data, and parse the test data based on the data parsing process; wherein, generating a data parsing process corresponding to the test data includes: generating an identifier pointing to different parsing configuration file or rule set versions, the identifier being used to determine the parsing configuration file or rule set version corresponding to the current test condition parameter set, so that subsequent data parsing processes match the test condition parameter set; The test data is then assigned a credibility rating.

[0013] Furthermore, the set of parameters for supplementary testing includes at least one of the following: frequency point set to be supplemented, bias scan range, bias scan step, temperature point set, temperature step, and stability criterion.

[0014] Furthermore, the credibility identifier is determined based on the proportion of satisfied rule items, the degree of gaps, and / or the consistency index of historical data under the same conditions.

[0015] On the other hand, this invention discloses a system for determining and processing the resolvability of semiconductor defect test data, the system comprising: The data acquisition module is used to acquire test data and test condition information corresponding to the test data; The test condition parsing module is used to parse the test condition information and construct a set of test condition parameters; The parsability determination and result output module is used to call preset parsing rules to match and judge the set of test condition parameters, and output the parsability determination result of the test data; The processing instruction generation module is used to output condition gap information when it is determined that the test data is unparseable or the parsing conditions are insufficient, and to generate processing instructions based on the condition gap information. The parsing module is used to execute a data parsing process that matches the set of test condition parameters when it is determined that the test data meets the parsing conditions, so as to extract defect characterization parameters.

[0016] Compared with the prior art, the present invention has at least the following technical effects: In this embodiment, by introducing a parsing validity determination mechanism before extracting defect characterization parameters, the parsability of test data is automatically determined. Specifically, by constructing a set of test condition parameters and matching them with preset parsing rules, the rationality of the test conditions can be effectively evaluated. Furthermore, by determining the parsability of the test data before extracting defect characterization parameters, and by outputting condition gap information when the test data does not meet the parsing conditions, generating processing instructions based on the condition gap information, and controlling the output of the data parsing process that matches the set of test condition parameters through the processing instructions, the accuracy and applicability of semiconductor defect test data parsing are effectively improved. Finally, this scheme achieves a closed-loop process of data acquisition, parsability determination, processing instruction generation, and defect characterization parameter extraction, improving the overall automation level of the process. Attached Figure Description

[0017] Figure 1 This is a simplified flowchart illustrating the semiconductor defect test data parseability determination and processing method in Embodiment 1 of the present invention; Figure 2 This is a simplified schematic diagram of the semiconductor defect test data parsability determination and processing system in Embodiment 2 of the present invention. Detailed Implementation

[0018] The following description, with reference to schematic diagrams, illustrates a method and system for determining and processing the resolvability of semiconductor defect test data according to the present invention. Preferred embodiments of the invention are shown, and 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.

[0019] 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.

[0020] Example 1 Please refer to Figure 1 This embodiment discloses a method for determining and processing the resolvability of semiconductor defect test data, the method comprising: S1. Obtain test data and corresponding test condition information; S2. Parse the test condition information and construct a set of test condition parameters; S3. Call the preset parsing rules to match and judge the set of test condition parameters, and output the parsability judgment result of the test data; S4. If it is determined that the test data cannot be parsed or the parsing conditions are insufficient, output condition gap information and generate processing instructions based on the condition gap information; S5. If the test data is determined to meet the parsing conditions, execute the data parsing process that matches the set of test condition parameters to extract the defect characterization parameters.

[0021] In this embodiment, by introducing a parsing validity determination mechanism before extracting defect characterization parameters, the parsability of test data is automatically determined. Specifically, by constructing a set of test condition parameters and matching them with preset parsing rules, the rationality of the test conditions can be effectively evaluated. Furthermore, by determining the parsability of the test data before extracting defect characterization parameters, and by outputting condition gap information when the test data does not meet the parsing conditions, generating processing instructions based on the condition gap information, and controlling the output of the data parsing process that matches the set of test condition parameters through the processing instructions, the accuracy and applicability of semiconductor defect test data parsing are effectively improved. Finally, this scheme achieves a closed-loop process of data acquisition, parsability determination, processing instruction generation, and defect characterization parameter extraction, improving the overall automation level of the process.

[0022] In this embodiment, the preset parsing rules are judgment criteria pre-established based on the requirements of the DLTS / DLCP defect characterization parameter extraction algorithm and engineering practice experience. These rules are used to evaluate whether the test data meets the prerequisites for defect characterization parameter extraction. The preset parsing rules can be stored in the system's rule base and can be configured and updated according to different test scenarios, the type of material being tested, or the defect characterization parameter extraction algorithm.

[0023] In a specific example, the preset parsing rules include rules for the comparability of test conditions for multiple sets of test data, rules for meeting the sampling density of test data, and rules for adapting the coverage of test conditions. Of course, the specific preset parsing rules can be set according to actual application needs, test standard requirements, and the characteristics of the defect characterization parameter extraction algorithm, and no specific restrictions are imposed here.

[0024] In this embodiment, the defect characterization parameters refer to semiconductor deep-level defect-related parameters extracted using DLTS (deep-level transient spectrum) or DLCP (deep-level capacitance profile) techniques. Specifically, DLTS parameters include, but are not limited to, defect level location, defect trapping cross section, defect concentration, and defect thermal emission time constant; DLCP parameters include, but are not limited to, the depth distribution of carrier concentration, the depth distribution of trap concentration, and the interface state density.

[0025] In this embodiment, the data parsing process refers to a complete sequence of execution steps for processing and analyzing test data to extract defect characterization parameters, including a series of ordered operations such as data processing, signal feature extraction, parameter fitting, and result output.

[0026] In this embodiment, the test data mentioned in step S1 refers to the raw measurement data collected by the DLTS or DLCP test equipment during the test process. This data reflects the electrical response characteristics of the semiconductor device under test under specific test conditions.

[0027] In this embodiment, the test data includes at least one of capacitance signal and transient signal.

[0028] Among them, the capacitance signal refers to the junction capacitance value of the semiconductor device measured by the test equipment, which can be a steady-state capacitance value or a transient capacitance curve that changes with time; the transient signal refers to the response signal of the device capacitance changing with time after pulse excitation.

[0029] In actual testing, capacitance signals are usually presented as capacitance-time curves or capacitance-temperature curves, while transient signals are recorded as capacitance transient decay curves.

[0030] Furthermore, the test condition information mentioned in step S1 refers to the various test parameters set when performing DLTS or DLCP tests. These parameters directly affect the quality of test data acquisition and the accuracy of defect characterization parameter extraction.

[0031] In this embodiment, the test condition information includes at least one of the following: frequency information, bias voltage information, temperature information, AC excitation amplitude information, time window information, and sampling window information corresponding to the test data.

[0032] The frequency information includes, but is not limited to, the AC frequency value of the test signal, the frequency scanning range, the frequency step value, and the number of frequency points during multi-frequency testing.

[0033] The bias information includes, but is not limited to, DC bias voltage value, bias scan range, bias step value, amplitude and width of pulse bias, and bias scan mode (such as forward scan, reverse scan or bidirectional scan).

[0034] The temperature information includes, but is not limited to: test temperature point, temperature scan range, temperature step value, temperature stability criteria (such as the allowable range of temperature fluctuation and the stability time requirement), and heating and cooling rates.

[0035] The AC excitation amplitude information includes, but is not limited to, the peak amplitude, effective amplitude, and waveform type of the small-signal AC voltage applied to the semiconductor device under test.

[0036] The time window information includes, but is not limited to, time interval parameters used to analyze transient signals in DLTS testing, specifically including the start time, end time, width of the time window, and the number and distribution of time windows in multi-time window testing.

[0037] The sampling window information includes, but is not limited to, window parameters when digitally sampling transient signals, specifically including the sampling start time, sampling end time, sampling interval, and total number of sampling points.

[0038] Of course, those skilled in the art will understand that the types of test data and test condition information mentioned above, as well as the specific parameters included in each data and information, can be selected and adjusted according to actual test requirements, the type of semiconductor device under test, and the test standards adopted, and no specific restrictions are imposed here.

[0039] In this embodiment, the set of test condition parameters mentioned in step S2 is used to characterize the conditions for collecting test data.

[0040] In this embodiment, the set of test condition parameters includes at least one of the following: the frequency distribution of the electrical signal, the coverage of the test condition information, and the sampling density of the test data.

[0041] The frequency distribution includes, but is not limited to, the number of test frequency points, the arrangement of frequency points (such as linear uniform distribution or logarithmic interval distribution), the minimum and maximum values ​​of frequency coverage, and the relative spacing between frequency points during multi-frequency testing. Frequency distribution parameters are used to evaluate the completeness of test data in the frequency domain dimension and are an important basis for determining whether multi-frequency correlation analysis can be performed.

[0042] The coverage range of the test condition information refers to the range actually covered by each test parameter in its corresponding dimension, including the temperature coverage range formed by the lowest temperature to the highest temperature of the temperature scan, the bias coverage range formed by the minimum bias voltage to the maximum bias voltage of the bias voltage scan, the frequency coverage range formed by the lowest frequency to the highest frequency of the frequency test, etc., which are used to evaluate whether the test data covers the parameter space where the target defect may occur.

[0043] The sampling density of the test data includes, but is not limited to, temperature sampling density, bias sampling density, and time sampling density. The sampling density parameter is used to evaluate whether the test data has sufficient resolution to support accurate extraction of defect characterization parameters. Insufficient sampling density will lead to the loss of detailed information of signal features, thereby causing errors in the extraction of defect characterization parameters.

[0044] Of course, those skilled in the art can select and define appropriate test condition parameter sets according to the specific test methods, the test material system, and the requirements of the defect characterization parameter extraction algorithm, without making specific restrictions here.

[0045] In step S3, the step of calling preset parsing rules to match and judge the set of test condition parameters and outputting the parsability judgment result of the test data includes at least one of the following steps: Determine whether the test condition information corresponding to multiple sets of test data is comparable.

[0046] Determine whether the data point sampling density of the test data reaches the preset resolution.

[0047] Determine whether the coverage of the test condition information matches the extraction requirements of the defect characterization parameters.

[0048] The purpose of determining whether the test condition information corresponding to multiple sets of test data is comparable is to determine whether the conditions for horizontal comparative analysis are met between multiple sets of test data. When it is necessary to compare and analyze test data collected from different batches, different samples, or different times, the test condition information of each set of data should be consistent or comparable; otherwise, the comparison results will lose their meaning.

[0049] For example, for two sets of DLTS data collected from different batches, the test frequencies, temperature scan ranges, and bias settings will be compared. If the difference in key parameters exceeds a preset threshold, the two sets of data will be determined to be incomparable.

[0050] The purpose of determining whether the sampling density of the test data points reaches the preset analytical resolution is to determine whether the number and distribution of the test data sampling points meet the resolution requirements of the defect characterization parameter extraction algorithm. Insufficient sampling density will lead to increased interpolation error or inaccurate location of signal features during the defect characterization parameter extraction process.

[0051] For example, for DLTS data with temperature scanning, the temperature sampling density will be calculated. If the sampling density is lower than a preset threshold, it will be determined that the sampling density of the data does not meet the resolution requirements.

[0052] Determining whether the coverage of the test condition information matches the extraction requirements of the defect characterization parameters aims to evaluate whether the parameter space covered by the test conditions can fully present the characteristic signals of the target defect. If the coverage of the test condition information is insufficient, it will lead to incomplete extraction of defect characterization parameters or the generation of systematic errors.

[0053] For example, for a deep-level defect whose expected energy level is located 0.3eV-0.5eV below the conduction band, its signal peak will appear in a specific temperature range. The system checks whether the actual temperature scan range of the test covers this range. If the actual temperature range is 150K-350K, but the signal peak of the target defect appears near 380K, it is determined that the coverage of the test conditions does not meet the requirements for extracting the characterization parameters of the defect.

[0054] The S3 step enables the system to automatically assess the parsability of test data before extracting defect characterization parameters, avoiding the problem of distorted results caused by directly parsing test data that does not meet the conditions in traditional methods.

[0055] In this embodiment, the condition gap information mentioned in step S4 includes at least one of the following: a non-satisfied rule item identifier, a threshold or target interval corresponding to the non-satisfied rule item identifier, and the parameter missing amount corresponding to the non-satisfied rule item identifier.

[0056] The unmet rule item identifier includes, but is not limited to, coded identifiers for rule items that fail to meet preset parsing rules, such as rule number, rule name abbreviation; rule type classification labels, such as comparability rules, sampling density rules, coverage rules; and test condition dimension markers that trigger the identifier, such as temperature dimension, frequency dimension, etc. These unmet rule item identifiers are used to identify and precisely locate the specific rule items that cause the test data to fail to meet the parsing conditions, providing a clear direction for the generation of subsequent processing instructions.

[0057] The thresholds or target ranges corresponding to the non-met rule item identifiers include, but are not limited to, the preset pass / fail standards corresponding to each non-met rule item. Specifically, these include the minimum pass / fail threshold for that rule item, such as the minimum sampling density requirement; and the maximum limit value or target parameter range, such as the minimum range width of the effective temperature coverage. The thresholds or target ranges corresponding to the aforementioned non-met rule item identifiers are used to quantitatively express the gap between the current test conditions and the pass / fail standards, providing a reference for operators to understand the nature of the gap.

[0058] The missing parameter quantities corresponding to the non-met rule item identifiers include, but are not limited to, the quantized difference between the current test conditions and the target threshold or target range, such as the difference between the current sampling density and the minimum required density; and the relative deviation ratio, such as the missing percentage of the current temperature range and the target coverage range. The missing parameter quantities corresponding to the aforementioned non-met rule item identifiers are used to accurately describe the degree of inadequacy of the test conditions, supporting the system in automatically generating a set of suggested supplementary test parameters.

[0059] Furthermore, in step S4, the processing instructions include at least one of the following: Generate a set of supplementary test suggestion parameters corresponding to the test data, and perform supplementary tests based on the set of supplementary test suggestion parameters to complete the test condition information.

[0060] Generate a data parsing process corresponding to the test data, and parse the test data based on the data parsing process.

[0061] The test data is then assigned a credibility rating.

[0062] In step S4 above, the set of recommended parameters for supplementary testing includes at least one of the following: frequency point set to be supplemented, bias scan range, bias scan step, temperature point set, temperature step, and stability criterion.

[0063] Furthermore, in step S4 above, generating the data parsing process corresponding to the test data includes: generating identifiers pointing to different parsing configuration file or rule set versions, wherein the identifiers are used to determine the parsing configuration file or rule set version corresponding to the current test condition parameter set, so that the subsequent data parsing process matches the test condition parameter set.

[0064] Furthermore, in step S4 above, the credibility identifier is determined based on the proportion of satisfied rule items, the degree of gaps, and / or the consistency index of historical data with the same conditions.

[0065] The ratio of satisfied rule items refers to the ratio of the number of preset parsing rule items satisfied by the current test data to the total number of all rule items. The higher the ratio, the more dimensions the data meets the parsing requirements, and the higher the corresponding credibility.

[0066] The degree of gap refers to the magnitude of the deviation between the current test condition value and the target threshold or target range in each unmet rule item. The smaller the deviation, the closer the gap is to being met, and the smaller the corresponding credibility loss.

[0067] The consistency index of historical data under the same conditions refers to the statistical distribution consistency of historically collected data under the same or similar test conditions. The higher the consistency, the higher the degree of agreement between the current test data and historical patterns, and the higher the data credibility.

[0068] Through step S4, the system can provide processing instructions when test data does not meet the parsing conditions, effectively avoiding the distortion of results caused by directly extracting defect characterization parameters from unqualified data. On the one hand, by generating a set of supplementary test suggestion parameters, the system achieves automated and targeted completion of test conditions, reducing data invalidity caused by improper test parameter settings. On the other hand, generating a data parsing process corresponding to the test data avoids systematic errors introduced by the mismatch between the parsing algorithm and the test conditions. At the same time, the credibility identification mechanism provides engineers with a quantitative evaluation reference for the quality of the current test data, enabling them to reasonably judge the usability of test data in engineering scenarios with limited test conditions.

[0069] After completing the test condition information, the preset parsing rules can be called again to match and judge the updated test condition parameter set; when the updated test data meets the parsing conditions, step S5 is executed.

[0070] Example 2 Based on the same inventive concept, this embodiment discloses a semiconductor defect test data parsability determination and processing system. Please refer to [link / reference]. Figure 2 As shown, the system includes: The data acquisition module is used to acquire test data and test condition information corresponding to the test data.

[0071] The test condition parsing module is used to parse the test condition information and construct a set of test condition parameters.

[0072] The parsability determination and result output module is used to call preset parsing rules to match and judge the set of test condition parameters, and output the parsability determination result of the test data.

[0073] The processing instruction generation module is used to output condition gap information when it is determined that the test data is unparseable or the parsing conditions are insufficient, and to generate processing instructions based on the condition gap information.

[0074] The parsing module is used to execute a data parsing process that matches the set of test condition parameters when it is determined that the test data meets the parsing conditions, so as to extract defect characterization parameters.

[0075] It is understood that the above-mentioned semiconductor defect test data resolvability determination and processing system and the semiconductor defect test data resolvability determination and processing method 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 semiconductor defect test data resolvability determination and processing method can achieve has been described in detail in Embodiment 1, so it will not be repeated here.

[0076] 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 method for determining and processing the resolvability of semiconductor defect test data, characterized in that, The method includes: Acquire semiconductor defect test data and corresponding test condition information; The test condition information is parsed to construct a set of test condition parameters; The preset parsing rules are invoked to match and judge the set of test condition parameters, and the parsability judgment result of the semiconductor defect test data is output. The step of calling preset parsing rules to match and judge the set of test condition parameters and outputting the parsability determination result of the semiconductor defect test data includes at least one of the following: Determine whether the test condition information corresponding to multiple sets of semiconductor defect test data is comparable; Determine whether the data point sampling density of the semiconductor defect test data reaches the preset resolution; Determine whether the coverage of the test condition information matches the extraction requirements of the defect characterization parameters; if it is determined that the semiconductor defect test data cannot be parsed or the parsing conditions are insufficient, output condition gap information and generate processing instructions based on the condition gap information; The condition gap information includes at least one of the following: a rule item identifier that is not satisfied, a threshold or target interval corresponding to the rule item identifier that is not satisfied, and the number of missing parameters corresponding to the rule item identifier that is not satisfied. The processing instructions include generating a set of supplementary test suggestion parameters corresponding to the semiconductor defect test data, and / or generating a data parsing process corresponding to the semiconductor defect test data, and parsing the semiconductor defect test data based on the data parsing process; wherein generating a data parsing process corresponding to the semiconductor defect test data includes: generating an identifier pointing to different parsing configuration file or rule set versions, the identifier being used to determine the parsing configuration file or rule set version corresponding to the current test condition parameter set, so that subsequent data parsing processes match the test condition parameter set; If the semiconductor defect test data is determined to meet the parsing conditions, a data parsing process matching the test condition parameter set is executed to extract defect characterization parameters.

2. The method for determining and processing the resolvability of semiconductor defect test data as described in claim 1, characterized in that, The semiconductor defect test data includes at least one of capacitance signals and transient signals.

3. The method for determining and processing the resolvability of semiconductor defect test data as described in claim 1, characterized in that, The test condition information includes at least one of the following: frequency information, bias voltage information, temperature information, AC excitation amplitude information, time window information, and sampling window information corresponding to the semiconductor defect test data.

4. The method for determining and processing the resolvability of semiconductor defect test data as described in claim 1, characterized in that, The set of test condition parameters includes at least one of the following: frequency distribution of electrical signals, coverage of the test condition information, and sampling density of the semiconductor defect test data.

5. The method for determining and processing the resolvability of semiconductor defect test data as described in claim 1, characterized in that, The set of recommended parameters for supplementary testing includes at least one of the following: frequency point set to be supplemented, bias scan range, bias scan step, temperature point set, temperature step, and stability criterion.

6. The method for determining and processing the resolvability of semiconductor defect test data as described in claim 1, characterized in that, The processing instructions also include assigning a credibility rating to the semiconductor defect test data.

7. The method for determining and processing the resolvability of semiconductor defect test data as described in claim 6, characterized in that, The credibility indicator is determined based on the proportion of satisfied rule items, the degree of gaps, and / or the consistency index of historical data under the same conditions.

8. A system for determining and processing the resolvability of semiconductor defect test data, characterized in that, The system includes: The data acquisition module is used to acquire semiconductor defect test data and test condition information corresponding to the semiconductor defect test data; The test condition parsing module is used to parse the test condition information and construct a set of test condition parameters; The resolvability determination and result output module is used to call preset parsing rules to match and judge the test condition parameter set, and output the resolvability determination result of the semiconductor defect test data; The process of matching and judging the set of test condition parameters by invoking preset parsing rules and outputting the parsability determination result of the semiconductor defect test data includes at least one of the following steps: Determine whether the test condition information corresponding to multiple sets of semiconductor defect test data is comparable; Determine whether the data point sampling density of the semiconductor defect test data reaches the preset resolution; Determine whether the coverage of the test condition information matches the extraction requirements of the defect characterization parameters; The processing instruction generation module is used to output condition gap information when it is determined that the semiconductor defect test data is unresolvable or the parsing conditions are insufficient, and to generate processing instructions based on the condition gap information. The condition gap information includes at least one of the following: a rule item identifier that is not satisfied, a threshold or target interval corresponding to the rule item identifier that is not satisfied, and the number of missing parameters corresponding to the rule item identifier that is not satisfied. The processing instructions include generating a set of supplementary test suggestion parameters corresponding to the semiconductor defect test data, and / or generating a data parsing process corresponding to the semiconductor defect test data, and parsing the semiconductor defect test data based on the data parsing process; wherein generating a data parsing process corresponding to the semiconductor defect test data includes: generating an identifier pointing to different parsing configuration file or rule set versions, the identifier being used to determine the parsing configuration file or rule set version corresponding to the current test condition parameter set, so that subsequent data parsing processes match the test condition parameter set; The parsing module is used to execute a data parsing process that matches the set of test condition parameters when it is determined that the semiconductor defect test data meets the parsing conditions, so as to extract defect characterization parameters.

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