A BSIM-IMG noise model optimization method and device

Abstract

This application provides a BSIM-IMG noise model optimization method and apparatus, applied in the field of device parameter extraction and modeling technology. It includes: adding channel back interface parameters and carrier distribution parameters of an FDSOI MOSFET device. The channel back interface parameters include NOIA2, NOIB2, NOIC2, and interface trap parameters, while the carrier distribution parameters include the peak value of the carrier distribution. The BSIM-IMG noise model is then optimized based on these parameters to obtain an optimized BSIM-IMG noise model. Thus, the back-gate voltage affects the channel back interface parameters and carrier distribution parameters, which in turn affect the magnitude of the 1 / f noise. Optimizing the BSIM-IMG noise model based on these parameters, i.e., introducing the influence of the back-gate voltage on the noise of the channel back interface and carrier distribution, and fully considering the impact of the back-gate voltage on the 1 / f noise, improves the simulation accuracy of the optimized BSIM-IMG noise model, accurately reflecting the influence of the back-gate voltage on the noise.

Description

Technical Field

[0001] This application relates to the field of device parameter extraction and modeling technology, specifically to a BSIM-IMG noise model optimization method and apparatus. Background Technology

[0002] As the characteristics of core integrated circuit MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor) devices continue to shrink, traditional bulk silicon devices have reached their physical limits. FDSOI (Fully Depleted Silicon-On-Insulator) MOSFET devices offer advantages over traditional MOSFET devices, such as lower leakage current and adjustable voltage threshold, and show promising application prospects.

[0003] The FDSOI MOSFET device is modeled using the BSIM-IMG (Berkeley Short-channel IGFET Model - Independent Multi-Gate) model. As device dimensions decrease, the impact of low-frequency noise on simulation results becomes significant. Currently, the BSIM-IMG noise model is based on the assumption that 1 / f noise monotonically increases with increasing back-gate voltage, resulting in relatively low simulation accuracy. Summary of the Invention

[0004] In view of this, this application provides a BSIM-IMG noise model optimization method and apparatus. The optimized BSIM-IMG noise model takes into account the influence of back gate voltage on 1 / f noise, which can effectively improve the simulation accuracy of the model.

[0005] To solve the above problems, the technical solution provided in this application is as follows:

[0006] In a first aspect, this application provides a method for optimizing a BSIM-IMG noise model. The method includes: adding channel back interface parameters and carrier distribution parameters of an FDSOI MOSFET device, wherein the channel back interface parameters include NOIA2 trap density parameters, NOIB2 trap density parameters, NOIC2 trap density parameters, and interface trap parameters, and the carrier distribution parameters include carrier distribution peak values; optimizing the BSIM-IMG noise model based on the channel back interface parameters and the carrier distribution parameters to obtain an optimized BSIM-IMG noise model.

[0007] Based on the solution provided in the embodiment, the back gate voltage affects the back interface parameters of the channel and the carrier distribution parameters, which in turn affects the magnitude of 1 / f noise. The BSIM-IMG noise model is optimized based on the back interface parameters of the channel and the carrier distribution parameters. The optimized BSIM-IMG noise model can more accurately simulate 1 / f noise, thus improving the simulation accuracy of the BSIM-IMG noise model.

[0008] In one possible implementation, optimizing the BSIM-IMG noise model based on the channel back interface parameters and the carrier distribution parameters to obtain an optimized BSIM-IMG noise model includes:

[0009] The noise current power spectral density is calculated based on the NOIA2 trap density parameter, the NOIB2 trap density parameter, the NOIC2 trap density parameter, the interface trap parameter, and the carrier distribution peak value to obtain the optimized BSIM-IMG noise model. The noise current power spectral density is used to characterize the magnitude of the 1 / f noise parameter.

[0010] In one possible implementation, the method further includes:

[0011] The noise voltage power spectral density is determined based on the noise current power spectral density.

[0012] In one possible implementation, prior to adding the channel back interface parameters and carrier distribution parameters of the FDSOI MOSFET device, the method further includes:

[0013] The IV characteristic parameters of the optimized BSIM-IMG noise model were extracted using silicon device modeling software;

[0014] The IV characteristic fitting is completed based on the IV characteristic parameters.

[0015] In one possible implementation, the FDSOI MOSFET device is 22nm.

[0016] Secondly, this application provides a BSIM-IMG noise model optimization device, the device comprising:

[0017] The module is used to add channel back interface parameters and carrier distribution parameters of FDSOI MOSFET devices. The channel back interface parameters include NOIA2 trap density parameters, NOIB2 trap density parameters, NOIC2 trap density parameters, and interface trap parameters. The carrier distribution parameters include carrier distribution peak values.

[0018] The optimization module is used to optimize the BSIM-IMG noise model based on the back interface parameters of the channel and the carrier distribution parameters to obtain the optimized BSIM-IMG noise model.

[0019] In one possible implementation, the optimization module is specifically used to: calculate the noise current power spectral density based on the NOIA2 trap density parameter, the NOIB2 trap density parameter, the NOIC2 trap density parameter, the interface trap parameter, and the carrier distribution peak value to obtain an optimized BSIM-IMG noise model, wherein the noise current power spectral density is used to characterize the magnitude of the 1 / f noise parameter.

[0020] In one possible implementation, the device further includes:

[0021] The determination module is used to determine the noise voltage power spectral density based on the noise current power spectral density.

[0022] In one possible implementation, prior to adding the channel back interface parameters and carrier distribution parameters of the FDSOI MOSFET device, the device further includes:

[0023] The extraction module is used to extract the IV characteristic parameters of the optimized BSIM-IMG noise model using silicon device modeling software;

[0024] The determination module is also used to determine the completion of IV characteristic fitting based on the IV characteristic parameters.

[0025] In one possible implementation, the FDSOI MOSFET device is 22nm.

[0026] Thirdly, this application provides a BSIM-IMG noise model optimization device, including: a processor, a memory, and a system bus;

[0027] The processor and the memory are connected via the system bus;

[0028] The memory is used to store one or more programs, the one or more programs including instructions that, when executed by the processor, cause the processor to perform the BSIM-IMG noise model optimization method described in the first aspect above.

[0029] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a device, cause the device to perform the BSIM-IMG noise model optimization method described in the first aspect.

[0030] Therefore, this application has the following beneficial effects:

[0031] This application provides a BSIM-IMG noise model optimization method. First, it adds channel back interface parameters and carrier distribution parameters of the FDSOI MOSFET device. The channel back interface parameters include NOIA2 trap density parameters, NOIB2 trap density parameters, NOIC2 trap density parameters, and interface trap parameters. The carrier distribution parameters include the peak value of the carrier distribution. Then, it optimizes the BSIM-IMG noise model based on the channel back interface parameters and the carrier distribution parameters to obtain the optimized BSIM-IMG noise model. Thus, the back gate voltage affects the channel back interface parameters and carrier distribution parameters, which in turn affect the magnitude of the 1 / f noise. Optimizing the BSIM-IMG noise model based on the channel back interface parameters and carrier distribution parameters, i.e., introducing the noise influence of the back gate voltage on the channel back interface and carrier distribution, and fully considering the influence of the back gate voltage on the 1 / f noise, improves the simulation accuracy of the optimized BSIM-IMG noise model and can accurately reflect the influence of the back gate voltage on the noise. Attached Figure Description

[0032] Figure 1 A flowchart illustrating a BSIM-IMG noise model optimization method provided in an embodiment of this application;

[0033] Figure 2 This application provides a schematic diagram of IV characteristic curve fitting after model optimization.

[0034] Figure 3 A schematic diagram of a 1 / f noise fitting curve after model optimization is provided in an embodiment of this application;

[0035] Figure 4 The model noise current power spectral density S provided in the embodiments of this application id Comparison diagram;

[0036] Figure 5 The model noise voltage power spectral density S provided in the embodiments of this application vg Comparison diagram;

[0037] Figure 6 A schematic diagram of a BSIM-IMG noise model optimization device provided in an embodiment of this application;

[0038] Figure 7 This is a schematic diagram of the structure of the BSIM-IMG noise model optimization device provided in the embodiments of this application. Detailed Implementation

[0039] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0041] Currently, the core MOSFET devices in integrated circuits have reached their physical size limits, leading to the market entry of FDSOI MOSFET devices. Compared to traditional MOSFET devices, FDSOI MOSFET devices offer advantages such as lower leakage current and adjustable voltage threshold, showing promising application prospects.

[0042] The mainstream MOSFET device model in the industry is the BSIM model, and the corresponding FDSOI MOSFET device model is the BSIM-IMG model. As the size decreases, the impact of noise, especially low-frequency noise, on the simulation results becomes significant. Specifically, the 1 / f noise component of the BSIM-IMG model (i.e., the BSIM-IMG noise model) does not fully consider the influence of the back gate voltage on 1 / f noise. It only attributes the effect of the back gate voltage to current changes, assuming that 1 / f noise increases with increasing back gate voltage, without considering the physical mechanism of the back gate's influence. It ignores the impact of carrier distribution and the BOX layer interface on 1 / f noise, resulting in low simulation accuracy of the BSIM-IMG noise model. The inventors discovered that in actual test data, changes in the back gate voltage affect the magnitude of 1 / f noise. 1 / f noise does not monotonically increase or decrease with changes in back gate voltage; it may exhibit minimum values, which existing BSIM models cannot simulate. The simulation accuracy of the model directly affects the simulation results. Therefore, it is urgent to improve the simulation accuracy of the 1 / f noise part of the BSIM-IMG noise model.

[0043] This application provides a BSIM-IMG noise model optimization method and apparatus. First, channel back interface parameters and carrier distribution parameters of the FDSOI MOSFET device are added. The channel back interface parameters include NOIA2 trap density parameters, NOIB2 trap density parameters, NOIC2 trap density parameters, and interface trap parameters. The carrier distribution parameters include the peak value of the carrier distribution. Then, the BSIM-IMG noise model is optimized based on the channel back interface parameters and the carrier distribution parameters to obtain the optimized BSIM-IMG noise model. Thus, the back gate voltage affects the channel back interface parameters and carrier distribution parameters, which in turn affect the magnitude of the 1 / f noise. Optimizing the BSIM-IMG noise model based on the channel back interface parameters and carrier distribution parameters, i.e., introducing the noise influence of the back gate voltage on the channel back interface and carrier distribution, and fully considering the influence of the back gate voltage on the 1 / f noise, improves the simulation accuracy of the optimized BSIM-IMG noise model and can accurately reflect the influence of the back gate voltage on the noise.

[0044] To facilitate understanding of the technical solutions provided in the embodiments of this application, the following description, in conjunction with the accompanying drawings, illustrates a BSIM-IMG noise model optimization method and apparatus provided in the embodiments of this application.

[0045] See Figure 1 , Figure 1 This is a flowchart illustrating a BSIM-IMG noise model optimization method provided in an embodiment of this application. The method specifically includes steps S101-S102.

[0046] S101: Add channel back interface parameters and carrier distribution parameters for the FDSOI MOSFET device. The channel back interface parameters include NOIA2 trap density parameters, NOIB2 trap density parameters, NOIC2 trap density parameters, and interface trap parameters. The carrier distribution parameters include carrier distribution peak values.

[0047] In this embodiment, by introducing the NOIA2 trap density parameter, NOIB2 trap density parameter, NOIC2 trap density parameter, interface trap parameter, and carrier distribution peak value, the magnitude of 1 / f noise in the BSIM-IMG noise model is made related to the back gate voltage and the back interface. Specifically, the NOIA2, NOIB2, and NOIC2 trap density parameters are the trap density parameters of the BOX layer in the FDSOI MOSFET device; the interface trap parameter Cit2 is the interface trap parameter of the back interface, which is composed of the interface between the silicon film and the BOX layer; and the carrier distribution peak value is the parameter representing the peak value of the carrier distribution in the VBG0 channel.

[0048] The NOIA2 trap density parameters, NOIB2 trap density parameters, NOIC2 trap density parameters, and interface trap parameter Cit2 are noise-related terms of the back interface, directly affecting the noise level. VBG0 represents the location of the charge distribution peak point in the channel; adjusting VBG0 can change the carrier distribution in the channel.

[0049] These parameters are already integrated into the model and can be obtained and called through silicon device modeling software. The five parameters involved are added to the original model to modify the original model, i.e. optimize the original model. The embodiments of this application do not limit the specific implementation method of parameter acquisition, and can be selected according to actual needs.

[0050] S102: Optimize the BSIM-IMG noise model based on the back interface parameters of the channel and the carrier distribution parameters to obtain the optimized BSIM-IMG noise model.

[0051] The model was optimized by adding parameters related to the back gate voltage, which introduced the noise effect of the back gate voltage on the channel back interface and carrier distribution. Specifically, the 1 / f noise parameter calculated based on the carrier distribution and channel back interface parameters can accurately reflect the impact of the back gate voltage on noise, thereby improving the simulation accuracy of the model.

[0052] The following uses the back gate voltage V bg The explanation is provided within the range of 0 to 2V. In practical applications, the back gate voltage range can be selected according to requirements. The 1 / f noise mentioned below specifically refers to the noise at the channel back interface, that is, the noise at the interface between the silicon film and the BOX layer in the FDSOI MOSFET device.

[0053] In one possible implementation, optimizing the BSIM-IMG noise model based on the channel back interface parameters and carrier distribution parameters to obtain an optimized BSIM-IMG noise model includes: calculating the noise current power spectral density based on the NOIA2 trap density parameters, the NOIB2 trap density parameters, the NOIC2 trap density parameters, the interface trap parameters, and the carrier distribution peak value to obtain the optimized BSIM-IMG noise model, wherein the noise current power spectral density is used to characterize the magnitude of the 1 / f noise parameter.

[0054] Noise current power spectral density S id The noise current power spectral density S is used to characterize the magnitude of the 1 / f noise parameter. id The calculation involves variables R1 and R2, where R1 represents the influence of the channel front interface on the 1 / f noise parameter, and R2 represents the influence of the channel back interface on the 1 / f noise parameter. The calculation of R1 is detailed in the following formula (1), and the calculation of R2 is detailed in the following formula (4).

[0055]

[0056]

[0057]

[0058] Where, N fg This represents the amount of front gate charge. As an intermediate variable, XBG represents the proportion of carriers near the back interface of the channel, and N... inv XBG represents the total number of charge carriers in the channel. VBG0 is a parameter representing the peak value of the charge carrier distribution in the channel, i.e., the back gate voltage adjustment parameter, where VBG is the back gate voltage. Changes in the back gate voltage will cause changes in XBG, indicating that the back gate voltage affects the charge carrier distribution.

[0059] Regarding intermediate variables In the calculation formula, k is the Boltzmann constant, T is the temperature, q is the elementary charge, and C is the t-value. ox For oxide layer capacitance, C it This is an interface trap capacitor.

[0060]

[0061]

[0062] Where, N bg This represents the amount of back gate charge. As another intermediate variable, C box For the BOX layer capacitor, C it2 For back interface trap parameters.

[0063] Substitute parameters R1 and R2 into the calculation of the noise current power spectral density S. id See formula (6) below for details.

[0064]

[0065]

[0066]

[0067] Among them, I ds For drain-source current, μ eff The effective mobility is given by γ, where γ represents the tunneling coefficient, f is the frequency, L is the channel length, FN1 and FN2 are intermediate variables in the calculation process, and NOIA, NOIB, and NOIC are 1 / f noise parameters in the existing model. N0 is the source-end charge density, N l This represents the drain charge density.

[0068] Thus, the noise current power spectral density S, used to characterize the magnitude of the 1 / f noise parameter, can be obtained through the above calculations. id Noise current power spectral density S id It is directly proportional to the noise level. In one possible implementation, it can be determined based on the noise current power spectral density S. id Determine the noise voltage power spectral density S vg See formula (9) below for details.

[0069]

[0070] Among them, g 2 m is the square of the transconductance.

[0071] Noise current power spectral density S id and noise voltage power spectral density S vg Both can be used to characterize the magnitude of the 1 / f noise parameter.

[0072] As an example, the aforementioned FDSOI MOSFET device is 22nm. This application's embodiments are based on test data from a 22nm FDSOI MOSFET device, but the specific size of the FDSOI device is not limited; it can be selected according to actual needs.

[0073] In one possible implementation, before adding the channel back interface parameters and carrier distribution parameters of the FDSOI MOSFET device, the method further includes: extracting the IV characteristic parameters of the optimized BSIM-IMG noise model using silicon device modeling software; and determining the IV characteristic fitting based on the IV characteristic parameters.

[0074] In this embodiment, the silicon device modeling software can be MBP (Model Builder Program). Based on MBP software, device models and actual test data can be imported and parameters extracted. See [link to relevant documentation]. Figure 2 , Figure 2 This diagram illustrates the fitting of an optimized IV characteristic curve, as provided in an embodiment of this application. The IV characteristic parameters are used to determine that the IV characteristic fitting is complete before noise fitting can proceed. This IV characteristic fitting is used to confirm the accuracy of the current simulation results and is the basis for noise fitting. See also... Figure 3 , Figure 3 This is a schematic diagram of a 1 / f noise fitting curve after model optimization, provided in an embodiment of this application.

[0075] A comparison of the simulation results of the model before optimization, the simulation results of the model after optimization, and actual data under the condition of 100Hz is shown in the figure. Figure 4 and Figure 5 , Figure 4 The model noise current power spectral density S provided in the embodiments of this application id Comparison diagram, Figure 5 The model noise voltage power spectral density S provided in the embodiments of this application vg Comparison diagram. It can be seen that the model is validated using the IV characteristic curve and 1 / f noise fitting curve obtained from testing a 22nm FDSOI n-type MOSFET, at a back gate voltage V... bg Within the range of 0–2V, the optimized model fits the test results well, and the optimized model also shows good performance for the noise current power spectral density S. id and noise voltage power spectral density S vg The simulation results and trends are closer to the actual data. Compared with the existing models, the simulation accuracy is greatly improved, and the back gate voltage can accurately reflect the impact of noise.

[0076] Based on steps S101-S102 above, the channel back interface parameters and carrier distribution parameters of the FDSOI MOSFET device are added. The channel back interface parameters include NOIA2 trap density parameters, NOIB2 trap density parameters, NOIC2 trap density parameters, and interface trap parameters. The carrier distribution parameters include the peak value of the carrier distribution. Then, the BSIM-IMG noise model is optimized based on these parameters to obtain the optimized BSIM-IMG noise model. Thus, the back-gate voltage affects the channel back interface parameters and carrier distribution parameters, which in turn affect the magnitude of the 1 / f noise. Optimizing the BSIM-IMG noise model based on these parameters, i.e., introducing the noise impact of the back-gate voltage on the channel back interface and carrier distribution, and fully considering the influence of the back-gate voltage on the 1 / f noise, improves the simulation accuracy of the BSIM-IMG noise model and accurately reflects the influence of the back-gate voltage on the noise.

[0077] The foregoing embodiments of this application provide a BSIM-IMG noise model optimization method based on the above description. Next, this application also describes a BSIM-IMG noise model optimization apparatus, which performs the aforementioned... Figure 1 The method shown below will be followed by a description of the function of the BSIM-IMG noise model optimization device. A schematic diagram of the BSIM-IMG noise model optimization device is shown below. Figure 6 As shown, it includes an addition module 601 and an optimization module 602.

[0078] Add module 601, used to add channel back interface parameters and carrier distribution parameters of FDSOI MOSFET device. The channel back interface parameters include NOIA2 trap density parameters, NOIB2 trap density parameters, NOIC2 trap density parameters, and interface trap parameters. The carrier distribution parameters include carrier distribution peak value.

[0079] The optimization module 602 is used to optimize the BSIM-IMG noise model based on the back interface parameters of the channel and the carrier distribution parameters to obtain the optimized BSIM-IMG noise model.

[0080] In one possible implementation, the optimization module 602 is specifically used to: calculate the noise current power spectral density based on the NOIA2 trap density parameter, the NOIB2 trap density parameter, the NOIC2 trap density parameter, the interface trap parameter, and the carrier distribution peak value to obtain an optimized BSIM-IMG noise model, wherein the noise current power spectral density is used to characterize the magnitude of the 1 / f noise parameter.

[0081] In one possible implementation, the device further includes:

[0082] The determination module is used to determine the noise voltage power spectral density based on the noise current power spectral density.

[0083] In one possible implementation, prior to adding the channel back interface parameters and carrier distribution parameters of the FDSOI MOSFET device, the device further includes:

[0084] The extraction module is used to extract the IV characteristic parameters of the optimized BSIM-IMG noise model using silicon device modeling software;

[0085] The determination module is also used to determine the completion of IV characteristic fitting based on the IV characteristic parameters.

[0086] In one possible implementation, the FDSOI MOSFET device is 22nm.

[0087] This application provides a BSIM-IMG noise model optimization device, which includes an adding module and an optimization module. The adding module adds channel back interface parameters and carrier distribution parameters of an FDSOI MOSFET device. The channel back interface parameters include NOIA2 trap density parameters, NOIB2 trap density parameters, NOIC2 trap density parameters, and interface trap parameters. The carrier distribution parameters include the carrier distribution peak value. The optimization module optimizes the BSIM-IMG noise model based on the channel back interface parameters and the carrier distribution parameters to obtain an optimized BSIM-IMG noise model. Thus, the back gate voltage affects the channel back interface parameters and carrier distribution parameters, which in turn affect the magnitude of the 1 / f noise. The BSIM-IMG noise model is optimized based on the channel back interface parameters and carrier distribution parameters. Specifically, the influence of back gate voltage on the noise of the channel back interface and carrier distribution is introduced, and the influence of back gate voltage on 1 / f noise is fully considered. The optimized BSIM-IMG noise model improves the simulation accuracy and can accurately reflect the influence of back gate voltage on noise.

[0088] See Figure 7 , Figure 7 This is a schematic diagram of the structure of a BSIM-IMG noise model optimization device disclosed in an embodiment of this application. Based on the BSIM-IMG noise model optimization method provided in the above method embodiment, this application embodiment also provides a BSIM-IMG noise model optimization device, including: a processor, a memory, and a system bus;

[0089] The processor and the memory are connected via the system bus;

[0090] The memory is used to store one or more programs, the one or more programs including instructions that, when executed by the processor, cause the processor to perform the BSIM-IMG noise model optimization method described in any of the above embodiments.

[0091] Based on the BSIM-IMG noise model optimization method provided in the above-described method embodiments, this application also provides a computer-readable storage medium storing instructions that, when executed on a device, cause the device to perform the BSIM-IMG noise model optimization method described in any of the above embodiments.

[0092] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0093] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0094] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing a BSIM-IMG noise model, characterized in that, The method includes: Add channel back interface parameters and carrier distribution parameters for FDSOI MOSFET devices. The channel back interface parameters include NOIA2 trap density parameters, NOIB2 trap density parameters, NOIC2 trap density parameters, and interface trap parameters. The carrier distribution parameters include carrier distribution peak values. The BSIM-IMG noise model is optimized based on the channel back interface parameters and the carrier distribution parameters to obtain the optimized BSIM-IMG noise model. The optimization of the BSIM-IMG noise model based on the channel back interface parameters and the carrier distribution parameters to obtain the optimized BSIM-IMG noise model includes: The noise current power spectral density S is calculated based on the NOIA2 trap density parameters, the NOIB2 trap density parameters, the NOIC2 trap density parameters, the interface trap parameters, and the carrier distribution peak value. id The optimized BSIM-IMG noise model was obtained, and the noise current power spectral density S id Used to characterize the magnitude of the 1 / f noise parameter; ； ； ； ； ； ； ； ； Where k is the Boltzmann constant, T is the temperature, and q is the elementary charge. This is the drain-source current. For effective migration rate, This represents the tunneling coefficient, where f is the frequency and L is the tunnel length. R1 represents the oxide layer capacitance, R2 represents the effect of the channel front interface on the 1 / f noise parameter, and FN1, FN2, ... , NOIA, NOIB, and NOIC are intermediate variables in the calculation process, representing 1 / f noise parameters in the existing model. Source-end charge density, The drain charge density; This represents the amount of front gate charge. The proportion of carriers near the back interface of the channel. The total number of charge carriers in the channel. VBG0 is the back gate charge quantity; VBG0 is a parameter representing the peak value of the carrier distribution in the channel, i.e., the back gate voltage adjustment parameter, where VBG is the back gate voltage. For interface trap capacitors; For BOX layer capacitors, For back interface trap parameters.

2. The method according to claim 1, characterized in that, The method further includes: The noise voltage power spectral density is determined based on the noise current power spectral density.

3. The method according to claim 1, characterized in that, The method further includes: The IV characteristic parameters of the optimized BSIM-IMG noise model were extracted using silicon device modeling software; The IV characteristic fitting is completed based on the IV characteristic parameters.

4. The method according to claim 1, characterized in that, The FDSOI MOSFET device is 22nm.

5. A BSIM-IMG noise model optimization device, characterized in that, The device includes: An add module is used to add channel back interface parameters and carrier distribution parameters of FDSOI MOSFET devices. The channel back interface parameters include NOIA2 trap density parameters, NOIB2 trap density parameters, NOIC2 trap density parameters, and interface trap parameters. The carrier distribution parameters include carrier distribution peak values. The optimization module is used to optimize the BSIM-IMG noise model based on the back interface parameters of the channel and the carrier distribution parameters to obtain the optimized BSIM-IMG noise model. The optimization module is specifically used to: calculate the noise current power spectral density S based on the NOIA2 trap density parameter, the NOIB2 trap density parameter, the NOIC2 trap density parameter, the interface trap parameter, and the carrier distribution peak value. id The optimized BSIM-IMG noise model was obtained, and the noise current power spectral density S id Used to characterize the magnitude of the 1 / f noise parameter; ； ； ； ； ； ； ； ； Where k is the Boltzmann constant, T is the temperature, and q is the elementary charge. This is the drain-source current. For effective migration rate, This represents the tunneling coefficient, where f is the frequency and L is the tunnel length. R1 represents the oxide layer capacitance, R2 represents the effect of the channel front interface on the 1 / f noise parameter, and FN1, FN2, ... , NOIA, NOIB, and NOIC are intermediate variables in the calculation process, representing 1 / f noise parameters in the existing model. Source-end charge density, The drain charge density; This represents the amount of front gate charge. The proportion of carriers near the back interface of the channel. The total number of charge carriers in the channel. VBG0 is the back gate charge quantity; VBG0 is a parameter representing the peak value of the carrier distribution in the channel, i.e., the back gate voltage adjustment parameter, where VBG is the back gate voltage. Interface trap capacitor; For BOX layer capacitors, For back interface trap parameters.

6. The apparatus according to claim 5, characterized in that, The device further includes: The determination module is used to determine the noise voltage power spectral density based on the noise current power spectral density.

7. The apparatus according to claim 5, characterized in that, The device further includes: The extraction module is used to extract the IV characteristic parameters of the optimized BSIM-IMG noise model using silicon device modeling software; The determination module is also used to determine the completion of IV characteristic fitting based on the IV characteristic parameters.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code or instructions that, when executed on a computer, cause the computer to perform the method described in any one of claims 1-4.

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