Influence degree evaluation device, method, and program

Abstract

This influence degree evaluation device: identifies, on the basis of the configuration of a semiconductor package, a prediction formula for predicting a phenomenon that occurs in the semiconductor package, the prediction formula including a variable corresponding to each of a plurality of factors; calculates the amount of change in the phenomenon with respect to each of the plurality of factors by substituting a prescribed value for each of the plurality of factors into a formula for each of the factors, the formulas being obtained by partially differentiating the prediction formula with respect to each of the variables; and evaluates the degree of influence for each of the factors with respect to the phenomenon on the basis of the amount of change for each of the factors.

Description

Impact assessment device, method, and program

[0001] The present disclosure relates to an impact assessment device, an impact assessment method, and an impact assessment program.

[0002] 2. Description of the Related Art Conventionally, materials used in manufacturing semiconductor packages are selected in consideration of phenomena that occur in the semiconductor package depending on the design of the semiconductor package and the physical properties of the materials used.

[0003] For example, a method for selecting a material for a panel used in manufacturing a semiconductor package has been proposed, which selects a material capable of producing a panel with a sufficiently small amount of warpage (see Patent Document 1). The method described in Patent Document 1 relates to a method for selecting a material for a panel including a back coating layer, a number of semiconductor elements, a sealing layer, and an insulating layer. This method involves using structural analysis software to construct a virtual model of the panel, into which the properties of the materials constituting the back coating layer, the sealing layer, and the insulating layer have been input. This method also calculates the amount of warpage of the virtual model and identifies, through structural analysis, the properties of the materials constituting the back coating layer, the sealing layer, and the insulating layer that affect the amount of warpage of the panel. Based on the identified information, this method then selects at least one new material from the back coating layer, the sealing layer, and the insulating layer so as to reduce the amount of warpage of the virtual model.

[0004] Japanese Patent Application Laid-Open No. 2020-38924

[0005] In the past, there were methods for predicting phenomena occurring in semiconductor packages (e.g., warpage of the substrate) by taking into account each factor, but no quantitative evaluation of the extent to which each factor affects the phenomenon had been performed.

[0006] The present disclosure has been made in consideration of the above points, and aims to provide an impact assessment device, method, and program that can quantitatively assess the impact of each factor on phenomena occurring in semiconductor packages.

[0007] The impact assessment device according to the first aspect includes an identification unit that identifies a prediction formula for predicting a phenomenon occurring in a semiconductor package, the prediction formula including variables corresponding to each of a plurality of factors that affect the phenomenon, based on the configuration of the semiconductor package; a calculation unit that calculates the amount of change in the phenomenon for each of the plurality of factors by substituting a predetermined value for each of the plurality of factors into an equation for each of the factors obtained by partially differentiating the prediction formula with respect to each of the variables; and an evaluation unit that evaluates the impact of each of the factors on the phenomenon based on the amount of change for each of the factors.

[0008] The impact assessment method according to the second aspect is a method in which an identification unit identifies a prediction formula for predicting a phenomenon occurring in a semiconductor package, the prediction formula including variables corresponding to each of a plurality of factors that affect the phenomenon, based on the configuration of the semiconductor package; a calculation unit calculates the amount of change in the phenomenon for each of the plurality of factors by substituting a predetermined value for each of the plurality of factors into an equation for each factor obtained by partially differentiating the prediction formula with respect to each of the variables; and an evaluation unit evaluates the impact of each of the factors on the phenomenon based on the amount of change for each of the factors.

[0009] The impact assessment program according to the third aspect is a program for causing a computer to function as: an identification unit that identifies a prediction formula for predicting a phenomenon occurring in a semiconductor package, the prediction formula including variables corresponding to each of a plurality of factors that affect the phenomenon, based on the configuration of the semiconductor package; a calculation unit that calculates the amount of change in the phenomenon for each of the plurality of factors by substituting a predetermined value for each of the plurality of factors into an equation for each of the factors obtained by partially differentiating the prediction formula with respect to each of the variables; and an evaluation unit that evaluates the impact of each of the factors on the phenomenon based on the amount of change for each of the factors.

[0010] According to the impact assessment device, method, and program disclosed herein, it is possible to quantitatively assess the impact of each factor on a phenomenon occurring in a semiconductor package.

[0011] 1 is a block diagram showing a hardware configuration of an impact evaluation apparatus; FIG. 2 is a functional block diagram of the impact evaluation apparatus; FIG. 3 is a diagram showing an example of a simulation model; FIG. 4 is a diagram showing an example of a simulation model; FIG. 5 is a diagram showing an example of a comparison between an actual measured value of the amount of warpage and a predicted value by a prediction formula; FIG. 6 is a diagram showing an example of a prediction profile in an identified prediction formula; FIG. 7 is a diagram showing an example of the influence of each factor on warpage; and FIG. 8 is a flowchart showing an example of an impact evaluation process.

[0012] An example of this embodiment will be described below with reference to the drawings. In each drawing, identical or equivalent components and parts are designated by the same reference numerals. The dimensions and proportions of the drawings are exaggerated for the sake of explanation and may differ from the actual proportions. In the following embodiment, an example of a phenomenon occurring in a semiconductor package will be described, in which the degree of influence of each factor on warpage of a substrate of the semiconductor package is evaluated.

[0013] Fig. 1 is a block diagram showing the hardware configuration of an impact assessment device 10 according to this embodiment. As shown in Fig. 1, the impact assessment device 10 includes a CPU (Central Processing Unit) 12, a memory 14, a storage device 16, an input device 18, an output device 20, a storage medium reading device 22, and a communication I / F (Interface) 24. Each component is connected to each other via a bus 26 so as to be able to communicate with each other.

[0014] The storage device 16 stores an impact assessment program for executing the impact assessment process described below. The CPU 12 is a central processing unit that executes various programs and controls each component. That is, the CPU 12 reads the program from the storage device 16 and executes the program using the memory 14 as a work area. The CPU 12 controls each component and performs various arithmetic processes in accordance with the program stored in the storage device 16.

[0015] The memory 14 is configured with RAM (Random Access Memory) and serves as a working area for temporarily storing programs and data. The storage device 16 is configured with ROM (Read Only Memory), HDD (Hard Disk Drive), SSD (Solid State Drive), etc., and stores various programs including the operating system and various data.

[0016] The input device 18 is a device for performing various inputs, such as a keyboard, a mouse, etc. The output device 20 is a device for outputting various information, such as a display, a printer, etc. A touch panel display may be used as the output device 20 to function as the input device 18.

[0017] The storage medium reader 22 reads data stored in various storage media such as CD (Compact Disc)-ROM, DVD (Digital Versatile Disc)-ROM, Blu-ray Disc, USB (Universal Serial Bus) memory, etc., and writes data to the storage media. The communication I / F 24 is an interface for communicating with other devices, and uses standards such as Ethernet (registered trademark), FDDI, and Wi-Fi (registered trademark).

[0018] Next, the functional configuration of the impact assessment device 10 according to this embodiment will be described. Fig. 2 is a block diagram showing an example of the functional configuration of the impact assessment device 10. As shown in Fig. 2, the impact assessment device 10 includes, as its functional configuration, an identifying unit 32, a calculating unit 34, and an evaluating unit 36. Each functional configuration is realized when the CPU 12 reads out an impact assessment program stored in the storage device 16, expands it in the memory 14, and executes it.

[0019] The identification unit 32 identifies a prediction formula for predicting a phenomenon that occurs in the semiconductor package, the prediction formula including variables corresponding to each of a plurality of factors that contribute to the phenomenon, based on the configuration of the semiconductor package. In this embodiment, the plurality of factors that contribute to the warpage of the semiconductor package are the elastic modulus, coefficient of thermal expansion (CTE), and thickness of the core material of the substrate of the semiconductor package.

[0020] Specifically, the identification unit 32 acquires sample data to be input to the impact assessment device 10. The sample data is data on the amount of warpage of the semiconductor package in each pattern, obtained by an experiment or simulation in which multiple patterns of values ​​for each of multiple factors are set for the configuration of a specified semiconductor package.

[0021] 3 and 4 show an example of a simulation model for obtaining sample data through simulation. FIG. 3 is an external perspective view of a portion of a semiconductor package 40 (the portion indicated by the dashed line in the plan view shown in the upper left of FIG. 3 ). FIG. 4 is a cross-sectional view taken along line A-A in FIG. 3 . The semiconductor package 40 includes a substrate 50 including a core material 42, a build-up material 44, copper 46, and a solder resist 48, and a chip 52 protected by an underfill 54. In this simulation model, the dimensions (length, width, and thickness) of each component, the stacking positional relationship of each component, and the like are specified as the configuration of the semiconductor package 40. Note that it is not necessary for all of these factors to be specified as the configuration of the semiconductor package; it is sufficient to specify at least the ratio between the area of ​​the substrate 50 and the area of ​​the chip 52.

[0022] For example, for the core material 42 of the simulation model, the values ​​of elastic modulus, CTE, and thickness are varied as follows, and the results of simulating the amount of warpage of the substrate 50 for a total of 640 patterns are obtained as sample data: Elastic modulus: 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 200 GPa (16 types) CTE: 3, 4.5, 6, 7.5, 9 ppm / °C (5 types) Thickness: 200, 400, 600, 800, 1000, 1200, 1400, 1600 μm (8 types)

[0023] The identifying unit 32 performs statistical processing such as multiple regression analysis using the acquired sample data to identify coefficients included in the prediction formula for the amount of warpage u shown in the following formula (1): u = f(x) + f(y) + f(z) (1) In formula (1), f(x), f(y), and f(z) are functions of variables corresponding to thickness, elastic modulus, and CTE, respectively. More specifically, the prediction formula may be as shown in formula (2) below.

[0024] u = α １ +α ２ ×x+α ３ ×y+α ４ × z + (x-α ５ ) × ((x-α ５ ) × α ６ ) + (x-α ５ ) × ((y-α ７ ) × α ８ ) + (y-α ７ ) × ((y-α ７ ) × α ９ ) + (x-α ５ ) × ((z-α １０ ) × α １１ ) + (y-α ７ ) × ((z-α １０ ) × α １２ ) (2)

[0025] In equation (2), x is a variable corresponding to thickness, y is a variable corresponding to elastic modulus, z is a variable corresponding to CTE, α １ ~α １２That is, the specifying unit 32 substitutes the values ​​of the thickness, modulus of elasticity, and CTE of each pattern of the sample data and the amount of warpage u of each pattern obtained by simulation or the like into equation (2) and performs statistical processing such as multiple regression analysis to determine the coefficient α １ ~α １２ Identify.

[0026] Figure 5 shows an example of a comparison between the measured warpage value and the predicted value using the prediction formula. In this example, the R-squared value between the measured value and the predicted value is 0.97, which indicates that a good prediction formula has been identified. Figure 6 shows an example of a prediction profile using the specified prediction formula. As shown in Figure 6, the warpage changes curvilinearly with respect to the thickness and modulus of elasticity, and tends to change linearly with respect to the CTE, indicating that a prediction formula that is in line with actual conditions has been identified.

[0027] The calculation unit 34 calculates the amount of change in the phenomenon for each of the multiple factors by substituting a predetermined value for each of the multiple factors into an equation for each factor obtained by partially differentiating the prediction equation with respect to each variable. For example, in the case of the prediction equation shown in equation (2), the partial differential for the thickness ∂u / ∂x is shown in equation (3) below, the partial differential for the elastic modulus ∂u / ∂y is shown in equation (4) below, and the partial differential for the CTE ∂u / ∂z is shown in equation (5) below.

[0028] ∂u / ∂x = α ２ + (x-α ５ ) × α ６ + (x-α ５ ) × α ６ + (y-α ７ ) × α ８ + (z-α １０ )×α11 (3) ∂u / ∂y=α ３ + (x-α ５ ) × α ８ + (y-α ７ ) × α ９ + (y-α ７ ) × α ９ + (z-α １０ )×α12 (4) ∂u / ∂z=α ４ + (x-α ５ ) × α １１+ (y-α ７ ) × α １２ (5)

[0029] The partial differential equation for each factor represents the change in warpage per unit when only the value of that factor is slightly changed. For example, the calculation unit 34 calculates the change in warpage for thickness (hereinafter referred to as "change (thickness)") by substituting predetermined values ​​of thickness, elastic modulus, and CTE into equation (3). Similarly, the calculation unit 34 calculates the change in warpage for elastic modulus (hereinafter referred to as "change (elastic modulus)") from equation (4), and calculates the change in warpage for CTE (hereinafter referred to as "change (CTE)") from equation (5).

[0030] The evaluation unit 36 ​​evaluates the influence of each factor on the phenomenon based on the amount of change for each factor. Specifically, the evaluation unit 36 ​​evaluates the amount of change for each factor with respect to the total amount of change for each factor as the influence of each factor on the phenomenon. More specifically, the evaluation unit 36 ​​calculates the influence of thickness (hereinafter referred to as "influence (thickness)") using the following formula (6). Similarly, the evaluation unit 36 ​​calculates the influence of elastic modulus (hereinafter referred to as "influence (elastic modulus)") using the following formula (7), and the influence of CTE (hereinafter referred to as "influence (CTE)") using the following formula (8).

[0031] Influence (thickness) = Change (thickness) / Σ ｉ Change (i) (6) Influence (elastic modulus) = Change (elastic modulus) / Σ ｉ Change (i) (7) Impact (CTE) = Change (CTE) / Σ ｉ Change (i) (8) Σ ｉ Change (i) = Change (thickness) + Change (elastic modulus) + Change (CTE) Note that the method for calculating the influence of each factor is not limited to the above example.

[0032] FIG. 7 shows an example of the influence of each factor on warpage. The left graph in FIG. 7 shows the influence of each factor when the thickness and modulus of elasticity are varied (CTE is a fixed value), the center graph shows the influence of each factor when the thickness and CTE are varied (modulus of elasticity is a fixed value), and the right graph shows the influence of each factor when the modulus of elasticity and CTE are varied (thickness is a fixed value). In the example of FIG. 7, CTE has the greatest influence on warpage. Regardless of which factor's value is varied, the ranking of influence is CTE > modulus of elasticity > thickness. In the example of FIG. 7, the influence (CTE) was approximately 85%, the influence (modulus of elasticity) was 10%, and the influence (thickness) was approximately 5%. The influence value of each factor varies depending on the prediction formula, i.e., the configuration of the semiconductor package.

[0033] Here, the advantages of evaluating the influence of each factor using an equation obtained by partially differentiating the prediction equation for the amount of warpage with a variable corresponding to each factor will be explained.

[0034] If partial differentiation is not applied, it is possible to compare the amount of change in the amount of warpage between sample data patterns. For example, the table below shows an example of the amount of warpage when simulating the values ​​of each factor in the following patterns 1 to 4.

[0035]

[0036] In the above table, the warpage reduction amount is the reduction amount of warpage when pattern 1 is used as the starting point. Pattern 2 is obtained by changing only the modulus of elasticity compared to pattern 1 (increased by 10 GPa). Pattern 3 is obtained by changing only the CTE compared to pattern 1 (decreased by 1.5 ppm / °C). Pattern 4 is obtained by changing only the thickness compared to pattern 1 (increased by 200 μm). The total change in warpage amount of patterns 2 to 4 compared to pattern 1 is 233 μm. As above, if the change amount of each factor with respect to the total change amount of each factor is defined as the influence of each factor, the influence of each factor is as follows:

[0037] Influence (thickness) = 38% (= 89 μm / 233 μm) Influence (elastic modulus) = 34% (= 79 μm / 233 μm) Influence (CTE) = 28% (= 65 μm / 233 μm)

[0038] The above influence cannot be properly evaluated because the conclusion changes depending on which pattern is used as the starting point and how much the value of each factor is changed from that starting pattern.

[0039] In this embodiment, the influence of each factor is evaluated using an equation obtained by partially differentiating the prediction formula for the amount of warpage with respect to a variable corresponding to each factor, so that the above-mentioned problems do not occur and the influence of each factor can be appropriately evaluated.

[0040] Next, the operation of the influence assessment device 10 according to this embodiment will be described.

[0041] Fig. 8 is a flowchart showing the flow of the impact assessment process executed by the CPU 12 of the impact assessment device 10. The CPU 12 reads out the impact assessment program from the storage device 16, expands it into the memory 14, and executes it, causing the CPU 12 to function as each functional component of the impact assessment device 10, and the impact assessment process shown in Fig. 8 is executed. Note that the impact assessment process is an example of the impact assessment method disclosed herein.

[0042] In step S10, the identification unit 32 acquires sample data, which is data on the amount of warpage of the semiconductor package, obtained by an experiment or simulation in which multiple patterns of values ​​for each of multiple factors are set for the specified semiconductor package configuration input into the impact assessment device 10.

[0043] Next, in step S12, the identification unit 32 substitutes the thickness, modulus of elasticity, and CTE values ​​of each pattern of the sample data and the warpage amount u of each pattern into a prediction formula for the warpage amount, such as shown in equation (2), and performs statistical processing such as multiple regression analysis to identify the coefficients of the prediction formula.

[0044] Next, in step S14, the calculation unit 34 calculates an equation by partially differentiating the prediction equation for the amount of warpage with variables corresponding to the thickness, modulus of elasticity, and CTE, respectively.The calculation unit 34 then calculates the change in thickness, modulus of elasticity, and CTE by substituting predetermined values ​​of the thickness, modulus of elasticity, and CTE into the equation for each partially differentiated factor.

[0045] In step S16, the evaluation unit 36 ​​evaluates the influence of each factor on the phenomenon by using the calculated change in thickness, change in elastic modulus, and change in CTE, and outputs the evaluation result, after which the influence evaluation process ends. The evaluation result may be, for example, the influence of each factor calculated using a predetermined value when the value of each factor is substituted for that value, or may be a graph such as that shown in FIG.

[0046] As described above, the influence assessment device according to this embodiment identifies a warpage prediction formula based on the configuration of the semiconductor package, the prediction formula including variables corresponding to each of a plurality of factors that contribute to the warpage of the semiconductor package. The influence assessment device then calculates the amount of change in warpage for each of the plurality of factors by substituting a predetermined value for each of the plurality of factors into an equation for each factor obtained by partially differentiating the prediction formula with respect to each of the variables. The influence assessment device then evaluates the influence of each factor on warpage based on the amount of change for each factor. This allows for quantitative evaluation of the influence of each factor on the warpage of the semiconductor package.

[0047] The user can use the evaluation results of the influence of each factor in the design and selection of materials for the semiconductor package. For example, the user can identify the factor with the greatest influence as the factor most effective in reducing warpage and select a material with a modified value for that factor. Furthermore, for example, if the user does not want to change the CTE value, the user can adjust the modulus of elasticity and thickness to achieve the desired amount of warpage, taking into account the influence of the modulus of elasticity and thickness.

[0048] A user interface may also be provided for accepting changes to the values ​​of each factor and the package configuration by the user. In this case, when the value of each factor is changed, the processes of steps S14 and S16 of the impact assessment process shown in Figure 8 can be repeated. When the package configuration is changed, the processes of steps S12 to S14 can be repeated.

[0049] In the above embodiment, the thickness, modulus of elasticity, and CTE of the core material have been described as examples of factors that contribute to warpage of the substrate of a semiconductor package, but the present invention is not limited to these. Other factors, such as the glass transition temperature (TG) of the core material, may also be considered. Furthermore, the influence of factors focusing on materials other than the core material may also be evaluated. For example, the influence of factors such as the thickness of the build-up film, the physical properties of the encapsulant (modulus of elasticity, CTE, thickness, etc.), and the number of wiring layers may also be evaluated.

[0050] Furthermore, in the above embodiment, the warpage of the substrate has been described as an example of a phenomenon occurring in a semiconductor package, but this is not limiting. For example, the phenomenon occurring in the semiconductor package may be the stress distribution occurring in the semiconductor package. In this case, each of the multiple factors may be the elastic modulus, CTE, thickness, TG, Poisson's ratio, etc. of the core material of the substrate of the semiconductor package. Furthermore, the phenomenon occurring in the semiconductor package may be the heat distribution occurring in the semiconductor package. In this case, each of the multiple factors may be the thermal conductivity, density, thickness, dielectric constant, etc. of the core material of the substrate of the semiconductor package. Furthermore, the present invention is not limited to these examples, and any factor that changes in response to the target phenomenon can be evaluated.

[0051] In addition, the impact assessment process executed by the CPU after loading the software (program) in the above embodiment may be executed by various processors other than the CPU. Examples of such processors include dedicated electrical circuits, such as programmable logic devices (PLDs) (such as field-programmable gate arrays (FPGAs)) whose circuit configuration can be changed after manufacture, and application-specific integrated circuits (ASICs) that are processors with circuit configurations specifically designed to execute specific processes. The impact assessment process may be executed by one of these various processors, or by a combination of two or more processors of the same or different types (e.g., multiple FPGAs, or a combination of a CPU and an FPGA). The hardware structure of these various processors is, more specifically, an electrical circuit that combines circuit elements such as semiconductor devices.

[0052] In the above embodiment, the impact assessment program is pre-stored (installed) in a storage device, but the present invention is not limited to this. The program may be provided in a form stored in a storage medium such as a CD-ROM, a DVD-ROM, or a USB memory. The program may also be downloaded from an external device via a network.

[0053] The following notes are provided regarding the above embodiment.

[0054] (Supplementary Item 1) An impact evaluation device including: an identification unit that identifies a prediction formula for predicting a phenomenon occurring in a semiconductor package based on a configuration of the semiconductor package, the prediction formula including variables corresponding to each of a plurality of factors; a calculation unit that calculates a change amount of the phenomenon for each of the plurality of factors by substituting a predetermined value for each of the plurality of factors into an equation for each of the factors obtained by partially differentiating the prediction formula with each of the variables; and an evaluation unit that evaluates the impact of each of the factors on the phenomenon based on the change amount for each of the factors.

[0055] (Supplementary Item 2) The influence evaluation device according to Supplementary Item 1, wherein the evaluation unit evaluates the amount of change for each of the factors with respect to the total amount of change for each of the factors as the influence of each of the factors on the phenomenon.

[0056] (Supplementary Item 3) The impact evaluation device described in Supplementary Item 1 or Supplementary Item 2, wherein the identification unit identifies the coefficients included in the prediction formula using the amount of change in the phenomenon obtained by an experiment or simulation in which multiple patterns of values ​​for each of the multiple factors are set for the specified semiconductor package configuration.

[0057] (Appendix 4) An impact evaluation device according to any one of appendixes 1 to 3, wherein when the phenomenon is warpage of the semiconductor package, each of the multiple factors includes the elastic modulus, thermal expansion coefficient, and thickness of a core material of a substrate of the semiconductor package.

[0058] (Appendix 5) An impact evaluation device according to any one of appendixes 1 to 3, wherein when the phenomenon is a stress distribution occurring in the semiconductor package, each of the plurality of factors includes the elastic modulus, thermal expansion coefficient, thickness, glass transition temperature, and Poisson's ratio of a core material of a substrate of the semiconductor package.

[0059] (Appendix 6) An impact evaluation device according to any one of appendices 1 to 3, wherein when the phenomenon is a heat distribution occurring in the semiconductor package, each of the plurality of factors includes the thermal conductivity, density, thickness, and dielectric constant of a core material of a substrate of the semiconductor package.

[0060] (Supplementary Item 7) The influence evaluation device according to any one of Supplementary Items 1 to 6, wherein the specifying unit receives a designation of a ratio between a substrate area and a chip area as a configuration of the semiconductor package.

[0061] REFERENCE SIGNS LIST 10 Impact evaluation device 12 CPU 14 Memory 16 Storage device 18 Input device 20 Output device 22 Storage medium reading device 24 Communication I / F 26 Bus 32 Identification unit 34 Calculation unit 36 ​​Evaluation unit 40 Semiconductor package 42 Core material 44 Build-up material 46 Copper 48 Solder resist 50 Substrate 52 Chip 54 Underfill

Claims

1. An impact evaluation device including: an identification unit that identifies a prediction formula for predicting a phenomenon occurring in a semiconductor package, the prediction formula including variables corresponding to each of a plurality of factors for the phenomenon, based on the configuration of the semiconductor package; a calculation unit that calculates a change amount of the phenomenon for each of the plurality of factors by substituting a predetermined value for each of the plurality of factors into an equation for each of the factors obtained by partially differentiating the prediction formula with respect to each of the variables; and an evaluation unit that evaluates the impact of each of the factors on the phenomenon based on the change amount for each of the factors.

2. The influence evaluation device according to claim 1, wherein the evaluation unit evaluates the amount of change for each of the factors relative to the total amount of change for each of the factors as the influence of each of the factors on the phenomenon.

3. The impact evaluation device according to claim 1, wherein the identification unit identifies the coefficients included in the prediction formula using the amount of change in the phenomenon obtained by an experiment or simulation in which multiple patterns of values ​​for each of the multiple factors are set for the specified semiconductor package configuration.

4. An impact evaluation device according to any one of claims 1 to 3, wherein when the phenomenon is warpage of the semiconductor package, each of the multiple factors includes the elastic modulus, thermal expansion coefficient, and thickness of the core material of the substrate of the semiconductor package.

5. An impact evaluation device according to any one of claims 1 to 3, wherein when the phenomenon is a stress distribution occurring in the semiconductor package, each of the multiple factors includes the elastic modulus, thermal expansion coefficient, thickness, glass transition temperature, and Poisson's ratio of a core material of a substrate of the semiconductor package.

6. An impact evaluation device according to any one of claims 1 to 3, wherein when the phenomenon is a heat distribution occurring in the semiconductor package, each of the plurality of factors includes the thermal conductivity, density, thickness, and dielectric constant of a core material of a substrate of the semiconductor package.

7. An impact assessment device according to any one of claims 1 to 3, wherein the specification unit accepts a specification of the ratio of the area of ​​the substrate to the area of ​​the chip as the configuration of the semiconductor package.

8. An impact evaluation method in which an identification unit identifies a prediction formula for predicting a phenomenon occurring in a semiconductor package, the prediction formula including variables corresponding to each of a plurality of factors that affect the phenomenon, based on the configuration of the semiconductor package; a calculation unit calculates the amount of change in the phenomenon for each of the plurality of factors by substituting a predetermined value for each of the plurality of factors into an equation for each of the factors obtained by partially differentiating the prediction formula with respect to each of the variables; and an evaluation unit evaluates the impact of each of the factors on the phenomenon based on the amount of change for each of the factors.

9. An impact evaluation program that causes a computer to function as: an identification unit that identifies a prediction formula for predicting a phenomenon occurring in a semiconductor package, the prediction formula including variables corresponding to each of a plurality of factors that affect the phenomenon, based on the configuration of the semiconductor package; a calculation unit that calculates the amount of change in the phenomenon for each of the plurality of factors by substituting a predetermined value for each of the plurality of factors into an equation for each of the factors obtained by partially differentiating the prediction formula with respect to each of the variables; and an evaluation unit that evaluates the impact of each of the factors on the phenomenon based on the amount of change for each of the factors.

Images