Power law noise generation method and generator

Abstract

The invention provides a power law noise generation method. The power law noise generation method comprises the steps of obtaining inflection point frequency fc and power spectrum density corresponding to a segmentation point based on a minimum frequency value, a maximum frequency value and a segmentation point number; obtaining a target amplitude, a first target state constant and a second target state constant of a Markov signal corresponding to the segmentation point based on the inflection point frequency and the power spectral density; regulating and controlling Read excitation based on the target amplitude, regulating and controlling Reset excitation based on the first target state constant, regulating and controlling Set excitation based on the second target state constant, randomly generating and reading a first state or a second state; n Markov signals are obtained and combined to obtain the target power law noise. According to the method, the power law noise is modeled into the envelope curves of the Markov signals, so that complex calculation operation is reduced, common calculation-intensive operation in a traditional software method is avoided, and the method has lower power consumption and lower calculation overhead.

Description

Technical Field

[0001] The present disclosure relates to the technical field of integrated circuits, and in particular to a method and a generator for generating power-law noise. Background Art

[0002] Power-law noise is a type of noise with special spectral characteristics. Its power spectrum density follows a power-law relationship with frequency, that is, ,in is a constant, usually called the power-law exponent. Power-law noise has important practical applications in many fields. In physics and complex system modeling, power-law noise is often used to simulate natural phenomena and random processes, such as seismic data, electrocardiogram signals, Internet traffic, etc. These natural and man-made systems often exhibit long-tail distribution characteristics or self-similarity, and power-law noise can provide an accurate noise model, which is helpful for studying the dynamic behavior of these complex systems. Therefore, the noise generation method needs to be able to accurately control the power spectral density and frequency characteristics of the noise to ensure that it conforms to a specific power-law exponent. , so that it can be effectively used in simulation and model analysis.

[0003] Current software methods for generating power-law noise typically involve computationally intensive software operations, such as filtering Gaussian noise using Fourier transforms or convolution operations to achieve specific power-law noise characteristics. Although these methods can effectively generate noise, their applications are limited in real-time, power consumption, and hardware implementation due to their high computational complexity. In recent years, some studies have reported on the generation of Gaussian noise, flicker noise, and Brownian noise through hardware implementation, which are specific cases of power-law noise, and some studies have successfully demonstrated the potential for accelerating the noise generation process through dedicated hardware (such as FPGAs and ASICs). Despite this, these existing schemes still have some limitations, such as the inability to achieve a wide range of adjustable power-law exponents ( The ability to generate power-law noise with adjustable power-law value is not achieved, and it is not possible to achieve efficient and flexible power-law noise generation under the premise of ensuring low power consumption and low circuit complexity. A general hardware solution with a wide range of values (from 0 to 2) and low energy consumption and low circuit complexity is still a difficult problem and research direction in current technology.

[0004] The information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] In order to solve the above technical problems, the purpose of this disclosure is to provide an efficient power-law noise generation method. This method models the power-law noise as the envelope curves of several Markov signals, cleverly utilizing the random characteristics of the Markov process to generate the noise signal. Unlike traditional Fourier transform or filter design methods, this method only uses a small amount of non-volatile memory to generate the Markov signal, thereby significantly reducing the demand for storage resources. During the generation process, by accurately combining the envelope curves of these Markov signals, the power-law exponent (0≤ ≤2) to meet the noise characteristics requirements of different applications. This method not only effectively reduces the complexity and power consumption of hardware design, but also provides flexible noise generation capabilities under the conditions of low power consumption and small hardware area.

[0006] In order to achieve the above objectives, the present disclosure adopts the following solutions.

[0007] A method for generating power-law noise comprises the following steps:

[0008] S100: Power law exponent of input target power law noise , minimum frequency value f min , maximum frequency value f max , the number of segmentation points N and the first target state constant and the second target state constant Ratio ;

[0009] S200: Based on the minimum frequency value f min , maximum frequency value f max , the number of segmentation points N is used to obtain the inflection point frequency f corresponding to the segmentation point c And the power spectrum density S(f c );

[0010] S300: Based on the inflection point frequency f c And the power spectrum density S(f c ) Get the target amplitude A of the Markov signal corresponding to the segmentation point, the first target state constant and the second target state constant ;

[0011] S400: Regulating the Read excitation based on the target amplitude A, and adjusting the Read excitation based on the first target state constant The Reset incentive is regulated based on the second target state constant Regulate Set incentives;

[0012] S500: Randomly generate a first state or a second state based on the regulated Read stimulus, the regulated Set stimulus, and the regulated Reset stimulus and read the state;

[0013] S600: Repeat S500 M times to obtain a Markov signal; the amplitude, first state constant and second state constant of the Markov signal are the target amplitude, first target state constant and and the second target state constant ;

[0014] S700: Repeat S300-S600 N times to obtain N Markov signals;

[0015] S800: Combine the obtained N Markov signals to obtain target power-law noise.

[0016] Optionally, in step S200, the inflection point frequency f corresponding to the split point is obtained according to the following formula: c :

[0017] .

[0018] Optionally, in step S200, the power spectrum density S(f c ):

[0019] .

[0020] Optionally, in step S300, the target amplitude A of the Markov signal is obtained according to the following formula:

[0021] .

[0022] Optionally, in step S300, the first target state constant of the Markov signal is obtained according to the following formula: :

[0023]

[0024] in, .

[0025] Optionally, in step S300, the second target state constant of the Markov signal is obtained according to the following formula: :

[0026]

[0027] in, .

[0028] Optionally, in step S400, the Read stimulus is regulated according to the following formula:

[0029]

[0030] in, Indicates the amplitude of the Read stimulus, Indicates the resistance value in high impedance state, Indicates the resistance value in the low-resistance state.

[0031] Optionally, in step S400 , before regulating the Set stimulus, the value of the Reset stimulus is fixed.

[0032] Optionally, in step S400, the Reset stimulus is regulated according to the following formula:

[0033]

[0034] in, represents the first target state constant, Indicates the probability of the first state transitioning to the second state, which is controlled by the Reset stimulus.

[0035] Optionally, in step S400, the Set incentive is regulated according to the following formula:

[0036]

[0037] in, represents the second target state constant, It represents the probability of the second state converting to the first state, which is controlled by the Set incentive.

[0038] Optionally, in step S500, the first state is a high-impedance state, and the second state is a low-impedance state.

[0039] A power-law noise generator, comprising:

[0040] Input unit, which is used to input the power law exponent of the target power law noise , minimum frequency value f min , maximum frequency value f max , the number of segmentation points N and the first target state constant and the second target state constant Ratio ;

[0041] Calculation unit for the power law exponent of the target power law noise based on the input , minimum frequency value f min , maximum frequency value f max And the number of segmentation points N calculates the inflection point frequency f corresponding to each segmentation point c And the power spectrum density S(fc ), input is based on the inflection point frequency f c And the power spectrum density S(f c ) The corresponding Markov signal target amplitude A and the first target state constant are calculated and the second target state constant ;

[0042] A control unit is configured to control the Read excitation based on the target amplitude A and the first target state constant The Reset incentive is regulated based on the second target state constant Regulate Set incentives;

[0043] A generation unit, which is used to randomly generate a first state or a second state based on the regulated Read stimulus, the regulated Set stimulus and the regulated Reset stimulus, and further generate a Markov signal; wherein the amplitude, the first state constant and the second state constant of the Markov signal are respectively the target amplitude, the first target state constant and the second target state constant calculated by the calculation unit.

[0044] The combination unit is used to combine and generate multiple Markov signals, and further generate target power-law noise.

[0045] A computer-readable storage medium is used to store a computer program, wherein the computer program is configured to implement the method when called by a processor.

[0046] An electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor; wherein the method is implemented when the processor executes the program.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. The disclosed power-law noise generation method reduces complex computational operations by modeling power-law noise as the envelope curves of several Markov signals, avoiding computationally intensive operations (such as Fourier transforms or convolutions) commonly found in traditional software-based approaches. Compared to existing software-based implementations, this hardware-implemented noise generation method offers lower power consumption and computational overhead.

[0049] 2. This method significantly reduces the complexity and power consumption of hardware implementation by using only a small amount of non-volatile memory to generate Markov signals and accurately combine them. Compared with existing hardware implementation methods (such as solutions using complex digital signal processors or large-scale storage units), this method significantly saves hardware area and circuit resources while ensuring the accuracy and flexibility of noise generation. In addition, the solution of this disclosure provides a more flexible power law index (0≤ ≤2) adjustment capability, enabling it to flexibly adjust the noise characteristics according to needs in various application scenarios. Existing hardware methods often find it difficult to provide such an adjustment range at low cost and low complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings illustrate exemplary embodiments of the present disclosure and together with the description serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0051] Figure 1 A schematic diagram of generating power-law noise according to an embodiment of the present disclosure;

[0052] Figure 2 This is an example diagram of Read incentive, Reset incentive, and Set incentive after regulation according to an embodiment of the present disclosure;

[0053] FIG3(a) is a schematic diagram showing the dependency of a state constant on a Reset stimulus after a Set stimulus is fixed according to an embodiment of the present disclosure; FIG3(b) is a schematic diagram showing the dependency of a state constant on a Set stimulus after a Reset stimulus is fixed;

[0054] Figure 4 This is a schematic diagram of the dependence of amplitude on Read excitation according to an embodiment of the present disclosure;

[0055] FIG5(a), FIG5(b), and FIG5(c) are example diagrams of three Markov signals MC1, MC2, and MC3 according to an embodiment of the present disclosure; FIG5(d) is an example diagram of a target power-law noise PLN obtained by combining according to an embodiment of the present disclosure;

[0056] Figure 6 The present invention is a schematic structural diagram of a power-law noise generator according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following is combined with Figures 1 to 6 The present disclosure is further described in detail with reference to the following embodiments. It is understood that the specific embodiments described herein are only used to explain the relevant content and are not intended to limit the present disclosure. It should also be noted that, for ease of description, only the parts relevant to the present disclosure are shown in the accompanying drawings.

[0058] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in the present disclosure can be combined with each other. The technical solution of the present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0059] Unless otherwise stated, the exemplary embodiments / examples shown are to be understood as providing exemplary features of various details of some ways in which the technical concepts of the present disclosure can be implemented in practice. Therefore, unless otherwise stated, the features of the various embodiments / examples may be further combined, separated, interchanged, and / or rearranged without departing from the technical concepts of the present disclosure.

[0060] The use of cross hatching and / or shading in the accompanying drawings is generally used to make the boundaries between adjacent components clear. As such, unless otherwise indicated, the presence or absence of cross hatching or shading does not convey or indicate any preference or requirement for the specific materials, material properties, dimensions, proportions, commonalities between the components shown, and / or any other characteristics, attributes, properties, etc. of the components. In addition, in the accompanying drawings, the sizes and relative sizes of the components may be exaggerated for clarity and / or descriptive purposes. When the exemplary embodiments can be implemented differently, the specific process sequence can be performed in a different order than described. For example, two successively described processes can be performed substantially simultaneously or in an order opposite to the order described. In addition, the same figure numbers represent the same components.

[0061] When a component is referred to as being “on,” “over,” “connected to,” or “coupled to” another component, the component may be directly on, directly connected to, or directly coupled to the other component, or intervening components may be present. However, when a component is referred to as being “directly on,” “directly connected to,” or “directly coupled to” another component, there are no intervening components present. For this purpose, the term “connected” may refer to a physical connection, an electrical connection, etc., with or without intervening components.

[0062] For descriptive purposes, the present disclosure may use spatially relative terms such as "below," "beneath," "under," "down," "above," "upper," "above," "higher," and "side (e.g., as in "sidewall")," to describe the relationship of one component to another (other) component as shown in the accompanying drawings. The spatially relative terms are intended to encompass different orientations of the device in use, operation, and / or manufacture in addition to the orientation depicted in the accompanying drawings. For example, if the device in the drawings is turned over, a component described as "below" or "beneath" another component or feature would then be positioned "above" the other component or feature. Thus, the exemplary term "below" can encompass both the "above" and "below" orientations. Furthermore, the device may be otherwise oriented (e.g., rotated 90 degrees or at other orientations), and as such, the spatially relative descriptors used herein should be interpreted accordingly.

[0063] The terms used herein are for the purpose of describing specific embodiments and are not intended to be restrictive. As used herein, unless the context clearly indicates otherwise, the singular forms "one (kind, person)" and "said (the)" are also intended to include plural forms. In addition, when the terms "comprise" and / or "include" and their variations are used in this specification, the features, integral bodies, steps, operations, parts, assemblies and / or their groups stated are explained, but the presence or addition of one or more other features, integral bodies, steps, operations, parts, assemblies and / or their groups is not excluded. It should also be noted that, as used herein, the terms "substantially", "approximately" and other similar terms are used as approximate terms and not as degree terms, so that they are used to explain the inherent deviations of the measured values, calculated values and / or the values provided that will be recognized by those of ordinary skill in the art.

[0064] In one embodiment, if Figure 1 As shown, the present disclosure provides a method for generating power-law noise, comprising the following steps:

[0065] S100: Power law exponent of input target power law noise , minimum frequency value f min , maximum frequency value f max , the number of segmentation points N and the first target state constant and the second target state constant Ratio ;

[0066] S200: Based on the minimum frequency value f min , maximum frequency value f max , the number of segmentation points N is used to obtain the inflection point frequency f corresponding to the segmentation point c And the power spectrum density S(f c );

[0067] Specifically, the minimum frequency value f min To the maximum frequency value f max The interval is divided into N+1 segments at equal distances, and the inflection point frequency f corresponding to each segmentation point is obtained. c , and the inflection point frequency f corresponding to each split point c Substitute the target power-law noise power spectral density function to obtain the corresponding power spectral density S(f c );

[0068] S300: Based on the inflection point frequency f c And the power spectrum density S(f c ) Get the target amplitude A of the Markov signal corresponding to the segmentation point, the first target state constant and the second target state constant ;

[0069] S400: Regulating the Read excitation based on the target amplitude A, and adjusting the Read excitation based on the first target state constant The Reset incentive is regulated based on the second target state constant Regulate Set incentives;

[0070] S500: Randomly generate a first state or a second state based on the regulated Read stimulus, the regulated Set stimulus, and the regulated Reset stimulus and read the state;

[0071] S600: Repeat S500 M times to obtain a Markov signal; the amplitude, first state constant and second state constant of the Markov signal are the target amplitude, first target state constant and and the second target state constant ;

[0072] S700: Repeat S300-S600 N times to obtain N Markov signals;

[0073] S800: Combine the obtained N Markov signals to obtain target power-law noise.

[0074] This embodiment models power-law noise as the envelope curves of several Markov signals. By generating Markov signals under specific pulse excitation, these Markov signals are combined to generate the target power-law noise. Compared with existing power-law noise generation techniques, this approach eliminates the need for computationally intensive software operations and allows the power-law exponent of the power-law noise to be independently controlled through electrical manipulation, significantly reducing hardware area and power consumption.

[0075] In one embodiment, in step S200, the inflection point frequency f corresponding to the split point is obtained according to the following formula: c :

[0076] .

[0077] In one embodiment, in step S200, the power spectrum density S (f c ):

[0078] ;

[0079] h is a constant that represents the amplitude of the noise.

[0080] In one embodiment, in step S300, the target amplitude A of the corresponding Markov signal is obtained according to the following formula:

[0081] .

[0082] In one embodiment, in step S300, the first target state constant of the corresponding Markov signal is obtained according to the following formula: :

[0083]

[0084] in, .

[0085] Preferably, in step S300, the second target state constant of the corresponding Markov signal is obtained according to the following formula: :

[0086]

[0087] in, .

[0088] In one embodiment, in step S400, the Read stimulus, the Reset stimulus, and the Set stimulus are all current stimulus or voltage stimulus; and the Read stimulus, the Reset stimulus, and the Set stimulus are all current stimulus or voltage stimulus.

[0089] In one embodiment, if Figure 2 As shown, the applied pulse excitation combination includes a reset excitation, a set excitation, and a read excitation. The generated signal can be regulated by adjusting the pulse amplitude or pulse width of the current or voltage of the reset excitation and the set excitation.

[0090] In one embodiment, in step S400 , before adjusting the Set stimulus, the Reset stimulus is fixed.

[0091] In one embodiment, in step S400, the Reset stimulus is regulated based on the following formula:

[0092]

[0093] in, represents the first target state constant, Indicates the probability of the first state transitioning to the second state, which is controlled by the Reset stimulus.

[0094] In calculating the first target state constant When the first state is converted to the second state, the probability , this probability is related to the Reset excitation amplitude, so the probability of the target first state converting to the second state can be obtained by adjusting the Reset excitation amplitude, and further the first target state constant can be obtained. , after obtaining the first target state constant The Reset incentive is fixed during the process.

[0095] In one embodiment, in step S400, the Set incentive is regulated based on the following formula:

[0096]

[0097] in, represents the second target state constant, It represents the probability of the second state converting to the first state, which is controlled by the Set incentive.

[0098] In one embodiment, as shown in FIG3(a) and FIG3(b), in step S400, the Set excitation and the Reset excitation are regulated to achieve the first state constant of the target Markov signal and the second state constant .

[0099] Figure 3(a) and Figure 3(b) show the first target state constant and the second target state constant Schematic diagram of the relationship between the combination of Set incentive and Reset incentive. Among them, Figure 3 (a) is the first target state constant Dependence on Set stimulus and Reset stimulus. When Set stimulus is fixed at 280mV and Reset stimulus is -340mV–-380mV, the first target state constant As the Reset excitation amplitude increases, it decreases. The Reset excitation is fixed at -360mV, the Set excitation is 260mV–300mV, and the first target state constant As the Set excitation amplitude increases, it does not change. Figure 3(b) shows the second target state constant. Dependence on Set stimulus and Reset stimulus. When Set stimulus is fixed at 280mV and Reset stimulus is -340mV–-380mV, the second target state constant As the Reset excitation amplitude increases, it increases. When the Reset excitation is fixed at -360mV and the Set excitation is 260mV–300mV, the second target state constant As the Set excitation amplitude increases, it decreases. It should be noted that other excitation amplitudes also conform to this dependence. The state constant has different dependencies on the pulse amplitudes of Set excitation and Reset excitation. As the Reset excitation amplitude increases, the first target state constant decreases, the second target state constant τstate2 increases. On the contrary, as the Set excitation amplitude increases, the first target state constant Remain unchanged, the second target state constant Decrease. That is, the first target state constant Only dependent on Reset stimulus, the second target state constant There is a dependency relationship between the Reset and Set incentives. Therefore, the Reset incentive needs to be fixed before the Set incentive is adjusted to obtain the second target state constant in the control measures.

[0100] In one embodiment, in step S400, the Read stimulus is regulated based on the following formula:

[0101]

[0102] Where A represents the target amplitude, Indicates the amplitude of the Read stimulus, Indicates the resistance value in high impedance state, Indicates the resistance value in the low-resistance state.

[0103] Figure 4 Schematic diagram of the relationship between target amplitude and Read excitation. Figure 4 In the example, the Read excitation is 50mV-300mV, and the target amplitude A increases linearly with the increase of the Read excitation amplitude.

[0104] In one embodiment, in step S500, the first state is a high-impedance state, and the second state is a low-impedance state.

[0105] In one embodiment, in step S800, the time domain signal of power-law noise is obtained by combining the N Markov signals. Exemplarily, the time domain signals of the N Markov signals are directly added to obtain the time domain signal of power-law noise.

[0106] In order to obtain the power spectral density , take three segmentation points and obtain three Markov signals MC1, MC2, and MC3, as shown in Figures 5(a) to 5(c). Combine them to obtain the target power-law noise PLN, as shown in Figure 5(d).

[0107] In one embodiment, if Figure 6 As shown, the present disclosure also provides a power-law noise generator, comprising:

[0108] Input unit, which is used to input the power law exponent of the target power law noise , minimum frequency value f min , maximum frequency value f max , the number of segmentation points N and the first target state constant and the second target state constant Ratio ;

[0109] Calculation unit for the power law exponent of the target power law noise based on the input , minimum frequency value f min , maximum frequency value f max And the number of segmentation points N calculates the inflection point frequency f corresponding to each segmentation point c And the power spectrum density S(f c ), based on the calculated inflection point frequency f c And the power spectrum density S(f c ) and then calculate the corresponding Markov signal target amplitude A and the first target state constant and the second target state constant ;

[0110] A control unit is configured to control the Read excitation based on the target amplitude A and the first target state constant The Reset incentive is regulated based on the second target state constant Regulate Set incentives;

[0111] A generating unit is configured to randomly generate a first state or a second state based on the regulated Read stimulus, the regulated Set stimulus, and the regulated Reset stimulus, and further generate a Markov signal.

[0112] The amplitude, the first state constant, and the second state constant of the Markov signal are respectively the target amplitude, the first target state constant, and the second target state constant calculated by the calculation unit.

[0113] The combination unit is used to combine and generate multiple Markov signals, and further generate target power-law noise.

[0114] In one embodiment, the control unit includes a microprocessor.

[0115] In one embodiment, the memory used by the generation unit is a new type of memory, including but not limited to magnetic random access memory, resistive random access memory, phase change memory, ferroelectric memory, and bidirectional threshold switch. Preferably, a magnetic random access memory is used, which has a sandwich structure and includes a free layer, a tunneling layer, and a pinned layer. The free layer and the pinned layer are composed of ferromagnetic materials, including but not limited to NiFe, CoFe, CoFeB, etc.; the tunneling layer is composed of non-magnetic insulating materials, including but not limited to MgO, Al2O3, Al2MgO4, ZnO, HfO2, and TaO2, etc. Preferably, both the free layer and the pinned layer are CoFeB, and the tunneling layer is MgO. The two states of the magnetic random access memory are: State 1 is a high resistance state with a resistance value of approximately 8kΩ, denoted as the AP state; State 2 is a low resistance state with a resistance value of approximately 4kΩ, denoted as the P state.

[0116] In one embodiment, the present disclosure further provides a computer-readable storage medium for storing a computer program, wherein the computer program is configured to implement the method when called by a processor.

[0117] In one embodiment, the present disclosure further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor; wherein the method is implemented when the processor executes the program.

[0118] In the description of this specification, the description with reference to the terms "one embodiment / method", "some embodiments / methods", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment / method or example are included in at least one embodiment / method or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment / method or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments / methods or examples. In addition, those skilled in the art may combine and combine different embodiments / methods or examples described in this specification and the features of different embodiments / methods or examples, unless they are contradictory.

[0119] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0120] Those skilled in the art will appreciate that the above embodiments are merely intended to clearly illustrate the present disclosure and are not intended to limit the scope of the present disclosure. Other changes or modifications may be made based on the above disclosure, and such changes or modifications are still within the scope of the present disclosure.

Claims

1. A method for generating power-law noise, wherein: The steps include: S100: Power law exponent of input target power law noise , minimum frequency value, maximum frequency value, number of division points N, and the ratio of the first target state constant to the second target state constant ; S200: Obtaining the inflection point frequency f corresponding to the segmentation point based on the minimum frequency value, the maximum frequency value, and the number of segmentation points N c And the power spectrum density S(f c ); S300: Based on the inflection point frequency f c And the power spectrum density S(f c ) obtaining a target amplitude A, a first target state constant, and a second target state constant of the Markov signal corresponding to the segmentation point; S400: Regulating the Read excitation based on the target amplitude A, and adjusting the Read excitation based on the first target state constant regulating the Reset stimulus, and regulating the Set stimulus based on the second target state constant; S500: Randomly generate a first state or a second state based on the regulated Read stimulus, the regulated Set stimulus, and the regulated Reset stimulus and read the state; S600: Repeat S500 M times to obtain a Markov signal; S700: Repeat S300-S600 N times to obtain N Markov signals; S800: Combine the obtained N Markov signals to obtain target power-law noise.

2. The method according to claim 1, wherein Preferably, in step S200, the inflection point frequency f corresponding to the split point is obtained according to the following formula: c : 。 3. The method according to claim 1, wherein In step S200, the power spectrum density S(f c ): ； Where h is a constant representing the amplitude of the noise.

4. The method according to claim 1, wherein In step S300, the target amplitude A of the Markov signal is obtained according to the following formula: 。 5. The method according to claim 1, wherein In step S300, the first target state constant of the Markov signal is obtained according to the following formula: : ； in, .

6. The method according to claim 1, wherein In step S300, the second target state constant of the Markov signal is obtained according to the following formula: : ； in, .

7. The method according to claim 1, wherein In step S400, the Read stimulus is regulated according to the following formula: ； in, Indicates the amplitude of the Read stimulus, Indicates the resistance value in high impedance state, Indicates the resistance value in the low-resistance state.

8. The method according to claim 1, wherein In step S400, the Reset stimulus is regulated according to the following formula: ； in, represents the first target state constant, Indicates the probability of the first state transitioning to the second state, which is controlled by the Reset stimulus.

9. The method according to claim 1, wherein: In step S400, the Set incentive is regulated according to the following formula: ； in, represents the second target state constant, It represents the probability of the second state converting to the first state, which is controlled by the Set incentive.

10. A power-law noise generator, wherein: include: Input unit, which is used to input the power law exponent of the target power law noise , minimum frequency value f min , maximum frequency value f max , the number of segmentation points N and the first target state constant and the second target state constant Ratio ; Calculation unit for the power law exponent of the target power law noise based on the input , minimum frequency value f min , maximum frequency value f max And the number of segmentation points N calculates the inflection point frequency f corresponding to each segmentation point c And the power spectrum density S(f c ), input is based on the inflection point frequency f c And the power spectrum density S(f c ) The corresponding Markov signal target amplitude A and the first target state constant are calculated and the second target state constant ; A control unit is configured to control the Read excitation based on the target amplitude A and the first target state constant The Reset incentive is regulated based on the second target state constant Regulate Set incentives; a generating unit configured to randomly generate a first state or a second state based on the regulated Read stimulus, the regulated Set stimulus, and the regulated Reset stimulus, and further generate a Markov signal; wherein the amplitude, the first state constant, and the second state constant of the Markov signal are respectively the target amplitude, the first target state constant, and the second target state constant calculated by the calculating unit; The combination unit is used to combine and generate multiple Markov signals, and further generate target power-law noise.

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