JP2013003069A - Method and device for evaluating thermal conductivity of thermally conductive composition, computer-readable recording medium, and program causing computer to execute the same - Google Patents

Abstract

PROBLEM TO BE SOLVED: To evaluate thermal conductivity taking account of a connection state of components of a thermally conductive composition.SOLUTION: A thermal conductivity evaluation device 30 of a thermally conductive composition includes: first means 31 for analyzing distribution information relating to thermal conduction of the thermally conductive composition by a two-dimensional DFA method to obtain an inclination of the two-dimensional DFA; and second means 32 for evaluating a fractal property of the thermally conductive composition by the inclination of the two-dimensional DFA that has been obtained by the first means 31 and evaluating the thermal conductivity of the thermally conductive composition on the basis of the evaluation result.

Description

  The present invention relates to a technique for evaluating thermal conductivity of a thermally conductive composition.

  Removal of heat from electronic components such as LSI and electronic devices such as computers is important for improving performance and stability. In particular, due to advances in information technology such as personal computers, mobile phones, tablet mobile terminals, smartphones, and cloud computing, the density and size of electronic devices are increasing, and heat dissipation is becoming more important. In addition, electronic components constituting an electronic device are also highly integrated, and in technologies such as three-dimensional stacking of semiconductor elements, heat generation density is increased, and thus heat radiation is becoming more important.

  Various heat conductive composite materials have been developed. In order to minimize the risk of short circuit, electronic components and electronic devices are required to have insulating properties as well as heat conductivity. For this reason, the heat conductive composite material which mix | blends a high heat conductive filler with insulating resin and uses it as a composite material is used. As the high thermal conductive filler, metals, ceramics, carbon materials, mixtures thereof, and composites can be used. In particular, in order to enhance insulation, it is preferable that the high thermal conductive filler is also insulative.

  The thermal conductivity of a single substance, especially metals and ceramics, varies depending on its state (solid / liquid / gas, crystal / amorphous / liquid crystal, orientation, and other higher-order structures). Any part is often used as a material having a uniform thermal conductivity, and the distribution and dispersion of components in a molded body of a single substance are rarely problematic in terms of performance.

  On the other hand, in composite materials, the thermal conductivity of the entire molded body is greatly affected by the components, especially the size, spatial distribution, and percolation of the components in the molded body of high thermal conductivity materials. give. That is, whether or not the heat conduction path is formed in the molded body and how it is formed (the thickness of the path related to the ease of heat passage and the continuity of the path) are important for the heat conductivity. It is.

  In addition, when there is a large distribution (difference) in the thermal conductivity in the molded body, the extent of the thermal conductivity in the region in contact with the heat generating part affects the heat dissipation performance or is greatly increased by secondary processing. When processing and taking out small pieces from a molded body, there is a problem that if the thermal conductivity of the taken-out part varies, the product yield will be reduced, or defective products with a predetermined thermal conductivity will be generated. There is also.

  As a heat conductive composition for forming the molded body, a heat conductive resin composition composed of heat conductive particles and a resin is known. In a heat conductive resin composition, it is calculated | required that content of a heat conductive particle is low from a viewpoint of productivity, for example.

  For various problems such as other desired physical properties such as high thermal conductivity and insulation, and productivity, etc., a structure composed of thermally conductive particles (for example, particle size, particle size distribution, particle shape) Although attempts have been made to solve this problem by controlling secondary particles obtained by preliminarily solidifying particles and percolation based on particle density, there is still room for study from the viewpoint of obtaining sufficiently high thermal conductivity.

  As such a thermally conductive resin composition, for example, a thermally conductive silicone resin composition containing thermally conductive particles such as zinc oxide, aluminum oxide, aluminum, etc., for example, the content of thermally conductive particles is nitrided A resin composition having a boron powder content of 92 mass% and a thermal conductivity of about 5 W / (m · K) is known (for example, see Patent Document 1).

Further, as the heat conductive resin composition, for example, a resin composition containing 10 to 35% by volume of carbon fiber and 1 to 20% by volume of boron nitride powder, the thermal conductivity of the molded body is about 2 A resin composition having a volume resistivity of 10 ² to 10 ⁹ Ω · cm is known (for example, see Patent Document 2).

  Further, as the thermally conductive resin composition, for example, a resin composition containing two types of mixed boron nitride powders having different particle characteristics such as surface area, particle size, and tap density, for example, the content of boron nitride powder There is known a resin composition having a thermal conductivity of 2 to 13 W / (m · K) of a molded article made of a resin composition having a volume of 40% by volume (see, for example, Patent Document 3).

  Further, as the thermally conductive resin composition, for example, a resin composition containing boron nitride powder having a specific particle size distribution, for example, a silicone resin composition having a boron nitride powder content of 40% by volume There is known a resin composition in which the thermal conductivity of a molded product by the method is less than 2 W / (m · K) (see, for example, Patent Document 4).

  Further, as the thermally conductive resin composition, for example, a resin composition containing a spherical boron nitride aggregate having irregular aspect ratios of less than 2 by binding irregular non-spherical particles with a binder, for example, containing boron nitride A resin composition having a heat conductivity of about 2 to 6 W / (m · K) by a resin composition having an amount of about 36% by mass is known (see, for example, Patent Document 5).

  Further, as the thermally conductive resin composition, for example, a resin composition containing boron nitride powder having a specific particle size and particle size distribution, for example, a resin having a boron nitride powder content of 55% by volume A resin composition having a thermal conductivity of a molded body of about 6 to 11 W / (m · K) is known (for example, see Patent Document 6).

  Further, as the heat conductive resin composition, for example, a poliobenzoxazine composition containing a special boron nitride powder which is an aggregated particle of flaky crystals having an average particle diameter of 225 μm, A resin composition having a heat conductivity of 32.5 W / (m · K) by a resin composition having a content of 78.5% by volume (88.0% by mass) is known (for example, (Refer nonpatent literature 1.).

JP 2009-221310 A JP 2008-266586 A Special table 2010-505729 JP 2005-343728 A Japanese translation of PCT publication No. 2008-510878 JP 2008-189818 A

Thermochimica Acta 320 (1998) 177-186

  As listed in Patent Documents 1-6 and Non-Patent Document 1, there are many examples in which thermal conductivity is improved by giving a structure to a molded body of a thermally conductive resin composition. In particular, in order to improve thermal conductivity while realizing insulation, heat conduction by transmission of crystal lattice vibration of heat conduction particles is required instead of heat conduction by vibration of free electrons.

  In the case of heat conduction by free electrons, electrons are interposed in the heat conduction between the heat conductive particles, so there may be a distance between the heat conduction particles, but in the case of heat conduction by lattice vibration, the lattice vibration It is required that the particles are in direct contact. In the case of direct contact, thermal conductivity according to the crystal lattice vibration in the particles can be obtained, and sufficient thermal conductivity can be obtained even if the contact area is small.

  In Patent Document 1, the heat conductive particles are more directly brought into contact with each other by containing the heat conductive particles densely. In Patent Document 2, particles are brought into direct contact with each other by interposing fiber-shaped heat conductive particles called carbon fibers in addition to heat conductive particles. However, in this case, electrical conductivity is generated in the thermally conductive resin composition by the carbon fiber having electrical conductivity. In Patent Document 3, the heat conductive particles are brought into direct contact with one type of case by interposing the small particles between the large particles with the heat conductive particles of two types. In this case, electrical conductivity does not occur. Patent Document 4 achieves the same effect as Patent Document 3 by providing particle distribution instead of two distinct types of heat conduction particles. In Patent Document 5, a heat conductive resin composition is obtained after directly contacting heat conductive particles in advance. Patent Document 6 obtains high thermal conductivity with little anisotropy by selecting the shape of the particles as compared with Patent Document 4 by particle contact with little anisotropy. In Non-Patent Document 1, the heat conductive particles are brought into direct contact with each other by making the shape into a complex shape such as a flake shape.

  As described in Patent Documents 1-6 and Non-Patent Document 1, the heat conductive composition is provided with a structure for bringing the heat conductive particles into direct contact with each other, thereby improving the heat conductivity. is doing. However, with the concept of Patent Document 1, the concepts of Patent Document 2-6 and Non-Patent Document 1 cannot be evaluated, and with the concept of Non-Patent Document 1, the concept of Patent Document 1-6 cannot be evaluated. The same. For this reason, in order to improve the efficient thermal conductivity and various properties of the molded body of the thermally conductive composition, a method having a concept that can be evaluated through the individual concepts is required.

  The value obtained by dividing the thermal conductivity by the specific heat and density of the substance is called the thermal diffusivity, and the square root value obtained by multiplying the thermal conductivity by the specific heat and density is called the thermal permeability. These are values that can be converted to each other, and are used properly for convenience of the measurement method.

  As a method for evaluating the heat dissipation characteristics of the thermally conductive composition, there is a method for obtaining thermal conductivity. The thermal conductivity can be obtained by measuring the thermal diffusivity by laser flash method, xenon flash method, periodic heating method, hot disk method, etc., and multiplying the specific heat and density of the composite material measured separately. it can.

  In the laser flash method, the surface of a sample is instantaneously and uniformly heated by pulsed laser light, and the temperature change on the back surface of the sample is measured with a radiation thermometer. In the xenon flash method, a xenon lamp is used as a heating source instead of laser light. In the periodic heating method, the surface of the sample is periodically and uniformly heated with a laser beam or a xenon lamp, and the temperature change on the back surface of the sample is also measured with a radiation thermometer. In the hot disk method, a constant current is passed through a sensor sandwiched between samples to generate heat, and the sensor voltage change at that time is measured.

  Except for the hot disk method, these methods are samples that have been formed into a thin piece with a thickness suitable for the measurement method, or cut into a thin piece so that the direction in which the thermal conductivity is to be measured by cutting or the like from the formed body is the thickness direction. , The thermal conductivity in the thickness direction (determined from the thermal diffusivity as described above) is measured. In this method, the averaged thermal conductivity in a region having a diameter of about several millimeters can be obtained as a physical property value as a composite material, but it is usually difficult to obtain the distribution in detail. In the hot disk method, since the sensor is sandwiched between samples, it is usually difficult to keep the distribution in detail.

  On the other hand, using the thermoreflectance in which the reflectance of light from the object surface changes with temperature, as in the measurement of thermal permeability using a thermophysical microscope, the sample is heated by the focused laser beam, A method has also been developed in which a change in temperature is detected from the intensity of reflected light from another focused laser beam, and the thermal permeation rate is measured as necessary from the point of a small area. In this method, the thermal conductivity can be obtained by obtaining the thermal permeability with a resolution of several μm in an area of several mm of a sample having a thickness of several mm.

  Although this method can determine the thermal conductivity distribution in the vicinity of the surface of the measurement surface, it does not evaluate the percolation state of the highly thermally conductive composition that is considered to affect the thermal conductivity.

  Although there are various methods for measuring the thermal conductivity of the composite material in this way, they are not measurement methods including information on the connection state of the heat conductive composition. The molded body of the thermally conductive composition is improved in thermal conductivity by forming a thermally conductive path with the thermally conductive particles constituting the molded body. For this reason, if the information of the connection state of a heat conductive composition is obtained, it will become possible to evaluate heat conductivity. Selection of composite material types, control of shape and powder characteristics, selection of mixing method and molding method, optimization of processing conditions, direction in secondary processing such as cutting, trimming, bonding and bonding And factors related to optimization of placement can be obtained clearly.

  It is also possible to obtain a two-dimensional thermal conductivity distribution by measurement with a thermophysical microscope, divide the distribution area on the lattice, and synthesize the thermal conductivity by the finite element method to evaluate the thermal conductivity. In this case, the resulting thermal conductivity is obtained, but the connection state for improving the thermal conductivity cannot be directly evaluated.

  The present invention has been made in view of such problems, and an object of the present invention is to evaluate the thermal conductivity of the thermally conductive composition in consideration of the connected state of the components of the thermally conductive composition. .

  As a result of intensive studies by the inventors, the distribution information on the heat conduction of the heat conductive composition obtained from the thermophysical microscope image is analyzed, and fractal information is obtained, thereby obtaining spatial information in the molded body of the heat conductive material. It was found that information on the arrangement and its connection state can be obtained.

(1) That is, in the thermal conductivity evaluation method for the thermal conductive composition of the present invention, a necessary calculation process is performed on the distribution information regarding the thermal conductivity of the thermal conductive composition, and the thermal conductive composition A first step of obtaining fractal information and a second step of evaluating the thermal conductivity of the thermal conductive composition based on the fractal information obtained in the first step are provided.

(2) Further, in the thermal conductivity evaluation method for a thermal conductive composition of the present invention, the first step may be a step of obtaining the fractal information by analyzing the distribution information by a two-dimensional DFA method. Good.

(3) In the thermal conductivity evaluation method for a thermal conductive composition of the present invention, the fractal information may be a two-dimensional DFA plot or a slope of a two-dimensional DFA obtained from a differential plot of the two-dimensional DFA plot. .

(4) Moreover, in the thermal conductivity evaluation method for the thermal conductive composition of the present invention, the second step is performed based on the gradient of the two-dimensional DFA obtained by the two-dimensional DFA method. It is good also as a step which evaluates fractal property and evaluates the heat conductivity of this heat conductive composition based on this evaluation result.

(5) Moreover, in the thermal conductivity evaluation method for the thermal conductive composition of the present invention, the first step may be a step of obtaining the fractal information by analyzing the distribution information by a box counting method. Good.

(6) Moreover, in the thermal-conductivity evaluation method of the heat conductive composition of this invention, it is good also considering the said fractal information as a fractal dimension.

(7) Moreover, in the thermal conductivity evaluation method for the thermal conductive composition of the present invention, the second step is to calculate the fractal property of the thermal conductive composition from the fractal dimension obtained by the box counting method. It is good also as a step which evaluates and evaluates the heat conductivity of this heat conductive composition based on this evaluation result.

(8) Moreover, in the thermal-conductivity evaluation method of the heat conductive composition of this invention, it is good also considering the said heat conductive composition as a heat conductive resin composition.

(9) Moreover, in the thermal conductivity evaluation method of the thermal conductive composition of the present invention, any one of thermal diffusivity distribution information, thermal conductivity distribution information, and thermal permeability distribution information is used as the distribution information related to the thermal conductivity. Such distribution information may be used.

(10) Moreover, the thermal conductivity evaluation apparatus of the thermal conductive composition of the present invention analyzes the distribution information regarding the thermal conductivity of the thermal conductive composition by the two-dimensional DFA method, thereby calculating the slope of the two-dimensional DFA. Fractal property of the thermally conductive composition is evaluated from the first means to be obtained and the inclination of the two-dimensional DFA obtained by the first means, and based on the evaluation result, the thermal conductivity of the thermally conductive composition is evaluated. And a second means for evaluating sex.

(11) Moreover, in the heat conductive evaluation apparatus of the heat conductive composition of this invention, it is good also considering the said heat conductive composition as a heat conductive resin composition.

(12) Moreover, in the thermal conductivity evaluation apparatus of the thermal conductive composition of the present invention, any one of thermal diffusivity distribution information, thermal conductivity distribution information, and thermal permeability distribution information is used as the distribution information related to the thermal conductivity. Such distribution information may be used.

(13) In the computer-readable recording medium of the present invention, the computer obtains the inclination of the two-dimensional DFA by analyzing the distribution information regarding the heat conduction of the thermally conductive composition by the two-dimensional DFA method. The thermal conductivity of the thermally conductive composition is evaluated from one means and the inclination of the two-dimensional DFA obtained by the first means, and based on the evaluation result, the thermal conductivity of the thermally conductive composition is determined. It is characterized in that a program for functioning as a second means for evaluation is recorded.

(14) Further, the program of the present invention includes a first means for obtaining a slope of a two-dimensional DFA by analyzing a distribution information relating to heat conduction of the thermally conductive composition by a two-dimensional DFA method, Second means for evaluating the fractal property of the thermally conductive composition from the inclination of the two-dimensional DFA obtained by the first means, and evaluating the thermal conductivity of the thermally conductive composition based on the evaluation result It is characterized by making it function as.

  According to the present invention, by obtaining fractal information from the distribution information related to the heat conduction of the heat conductive composition, the thermal conductivity is evaluated based on the information on the connection state of the components of the heat conductive composition. Can be done.

It is a figure which shows typically the hardware constitutions of the thermal conductivity evaluation apparatus as an example of embodiment. It is a figure which shows typically the structure of the thermophysical microscope as an example of embodiment. It is a figure which shows typically the functional block of the thermal conductivity evaluation apparatus as an example of embodiment. It is a flowchart for demonstrating the heat conductivity evaluation process in the heat conductivity evaluation apparatus as an example of embodiment. It is a flowchart for demonstrating the procedure of thermal conductivity evaluation which concerns on an Example. (A)-(c) is a schematic diagram for demonstrating preparation of the heat conductive composition which concerns on an Example. (A-1), (a-2) is a figure which shows the thermal diffusivity distribution of the measurement sample which concerns on an Example, (b-1), (b-2) is the measurement sample which concerns on an Example. It is a figure which shows the differential plot of the two-dimensional DFA plot of thermal diffusivity. It is a schematic diagram for demonstrating the thermal conductivity evaluation of the heat conductive composition which concerns on an Example.

Embodiments of the present invention will be described below.
[1. About thermal conductivity evaluation method]
The thermal conductivity evaluation method according to the present invention (hereinafter also referred to as this evaluation method) is a fractal information about a thermal conductive composition by performing a required calculation process on distribution information regarding thermal conductivity for the thermal conductive composition. The first step (fractal information acquisition step) to obtain the second step (thermal conductivity evaluation step) to evaluate the thermal conductivity of the thermally conductive composition based on the fractal information obtained in the first step I have.

  In addition, the thermal conductivity evaluation apparatus according to the present invention (hereinafter also referred to as the present evaluation apparatus) analyzes the distribution information related to the thermal conductivity of the thermal conductive composition by the two-dimensional DFA method, thereby tilting the two-dimensional DFA. The fractal property of the thermally conductive composition is evaluated from the first means (fractal information acquisition means) for obtaining the two-dimensional DFA obtained by the first means, and based on the evaluation result, the thermally conductive composition is evaluated. And a second means for evaluating the thermal conductivity of the object (thermal conductivity evaluation means).

  This evaluation method and this evaluation apparatus (hereinafter, this evaluation method and apparatus and this evaluation apparatus are collectively referred to as this evaluation method) are based on studies by the inventors, and distribution information relating to heat conduction of the thermally conductive composition is obtained. In the case of taking a fractal structure, that is, when the distribution information relating to the heat conduction of the heat conductive composition has fractal properties, it has been found and completed that the heat conductivity of the heat conductive composition can be evaluated to be high. is there.

  The reason why the thermal conductivity of the thermally conductive composition is high when the distribution information regarding the thermal conductivity of the thermally conductive composition has a fractal structure is not clear, but is considered to be due to the following mechanism. .

  The molded body of the heat conductive composition is composed of constituent components such as heat conductive particles or an assembly of the constituent components (for example, secondary particles obtained by agglomeration of the heat conductive particles (primary particles)). It is considered that by having a structure resulting from the size, spatial distribution, or percolation (connected) state of the agglomerated particles)), a thermal conductive path is formed, thereby improving the thermal conductivity. Here, in the case where a heat conductive path is formed inside the molded body of the heat conductive composition, the heat conduction observed on the surface of the heat conductive composition due to the formation of the heat conductive path. It is thought that regularity appears in the distribution information regarding. Therefore, by confirming the fractal nature of the distribution information related to the heat conduction of the heat conductive composition, if the distribution information related to the heat conduction takes a fractal structure, the distribution information related to the heat conduction does not have a regular structure. It can be judged that the thermal conductive path is formed inside the molded body of the thermal conductive composition. That is, from the distribution information regarding the heat conduction of the heat conductive composition, by confirming the fractal nature of the heat conductive composition, the formation state of the heat conductive path inside the heat conductive composition is predicted, Furthermore, it is considered that thermal conductivity can be evaluated.

In this evaluation method, it can be used for evaluation of a thermally conductive substance having a distribution (difference) in thermal conductivity. Especially, the composition or composite material which consists of two or more types of structural components is preferable, and it is used suitably for the heat conductive resin composition comprised from a heat conductive particle and resin, and its molded object. Of course, it can also be used to evaluate a single substance having a distribution (difference) in thermal conductivity.
Each of the above steps will be described in detail in the following sections.

[1-1. Fractal information acquisition step or fractal information acquisition means]
In the first step (fractal information acquisition step) or the first means (fractal information acquisition means), fractal information acquisition computer software (hereinafter referred to as software, computer program) is obtained for distribution information relating to heat conduction obtained from measurement by a thermophysical microscope. The fractal information about the heat conductive composition is obtained by analyzing using the same program.

  As distribution information related to heat conduction, thermal diffusivity distribution information may be used, heat conductivity distribution information may be used, or permeability distribution information may be used. These data are obtained as two-dimensional numerical information by measurement with a thermophysical microscope.

  The analysis of distribution information related to heat conduction can use a known method for obtaining fractal information from two-dimensional information, but it is preferable to use a two-dimensional DFA (Detrended Fracture Analysis) method or a box counting method.

  The slope of the two-dimensional DFA obtained from the two-dimensional DFA plot or the differential plot of the two-dimensional DFA plot by the two-dimensional DFA method, or the fractal dimension obtained by the box counting method is described later as fractal information about the thermally conductive composition. It can be used for the thermal conductivity evaluation step.

[1-2. Thermal conductivity evaluation step or thermal conductivity evaluation means]
In the second step (thermal conductivity evaluation step) or the second means (thermal conductivity evaluation means), based on the fractal information obtained in the first step or the first means, the thermal conductivity evaluation computer software is installed. It is used to evaluate the thermal conductivity of the thermally conductive composition.

  The two-dimensional DFA plot obtained by the two-dimensional DFA method or the gradient of the two-dimensional DFA obtained from the differential plot of the two-dimensional DFA plot, or the fractal dimension obtained by the box counting method is calculated for the thermally conductive composition. It can be used as fractal information.

  When the distribution information regarding the heat conduction for the heat conductive composition has a fractal structure, that is, when the distribution information regarding the heat conduction for the heat conductive composition has a fractal structure, the heat conductive composition has a fractal structure. It can be evaluated as having high thermal conductivity.

[1-3. About Thermally Conductive Composition]
The heat conductive resin composition used for this evaluation method is demonstrated.
Examples of the heat conductive particles contained in the heat conductive resin composition include zinc oxide, aluminum oxide, silica, silicon carbide, aluminum nitride, boron nitride, silver oxide, aluminum particles, silver particles, copper particles, tin particles, Examples include carbon fiber, graphite, fullerene, carbon nanofiber, silver nanorod, copper nanorod, and cellulose nanofiber. A size of 0.01 μm to 10 μm is used.

  The heat conductive particles may be used as primary particles as they are, or may be used as secondary particles obtained by hardening the primary particles and having a structure. Secondary particles can be pulverized by heating or natural drying after making the primary particles into water slurry (a fluid in the form of a mud made by putting solid particles in a liquid), or the water slurry is made into a fine mist. This is blown into hot air and dried instantaneously, or the aggregate of primary powders is heated at a temperature lower than the melting point and then pulverized to have a hardened structure for the purpose of improving fluidity and thermal conductivity. Can be obtained. The size of the secondary particles is 10 μm to 600 μm.

  As the resin, a resin component or a rubber component generally used in a molded body made of a heat conductive resin composition such as a heat radiating material can be used. One type of resin may be used, or two or more types may be used in combination.

  Examples of the resin component include thermoplastic resins and thermosetting resins. Examples of the thermoplastic resin include polyolefin resins such as polyethylene resin, polypropylene resin, and ethylene-vinyl acetate copolymer resin, polyester resins such as polyethylene terephthalate resin, polybutylene terephthalate resin, and liquid crystal polyester resin, polyvinyl chloride resin, and phenoxy. Resins, acrylic resins, polycarbonate resins, polyphenylene sulfide resins, polyphenylene ether resins, polyamide resins, polyamideimide resins, polyimide resins, polyetheramideimide resins, polyetheramide resins, and polyetherimide resins. Moreover, copolymers, such as those block copolymers and a graft copolymer, are also contained. Examples of the thermosetting resin include an epoxy resin, a silicone resin, and a phenol resin.

  Examples of the rubber component include natural rubber, polyisoprene rubber, styrene-butadiene copolymer rubber, polybutadiene rubber, ethylene-propylene copolymer rubber, ethylene-propylene-diene copolymer rubber, butadiene-acrylonitrile copolymer rubber. , Isobutylene-isoprene copolymer rubber, chloroprene rubber, silicone rubber, fluorine rubber, chlorosulfonated polyethylene, and polyurethane rubber.

  Examples of the epoxy resin include bisphenol A type epoxy resin, bisphenol F type epoxy resin, phenoxy type epoxy resin, biphenyl type epoxy resin, and polyfunctional type epoxy resin. Two or more types of epoxy resins may be mixed and used.

  The heat conductive resin composition which is the target of this evaluation method may contain additional components. Such additional components include, for example, functional resins obtained by imparting functionality to the above resins, such as liquid crystal epoxy resins, nitride particles such as aluminum nitride, silicon nitride, and fibrous boron nitride, alumina, and fibrous alumina. Insulating metal oxides such as zinc oxide, magnesium oxide, beryllium oxide and titanium oxide, insulating carbon components such as diamond and fullerene, resin curing agents, resin curing accelerators, and solvents.

  The resin curing agent is appropriately selected according to the type of resin used. For example, as the resin curing agent for epoxy resin, an acid anhydride curing agent and an amine curing agent can be used. Examples of the acid anhydride curing agent include tetrahydrophthalic acid anhydride, methyltetrahydrophthalic acid anhydride, hexahydrophthalic acid anhydride, and benzophenone tetracarboxylic acid anhydride. Examples of the amine curing agent include aliphatic polyamines such as ethylenediamine, diethylenetriamine, and triethylenetetramine, aromatic polyamines such as diaminodiphenylsulfone, diaminodiphenylmethane, diaminodiphenyl ether, and m-phenylenediamine, and dicyandiamide. If it is a hardening | curing agent for epoxy resins, it will mix | blend in the range of 0.3-1.5 normally by an equivalent ratio with respect to an epoxy resin.

  The resin curing accelerator is appropriately selected according to the type of resin used and the resin curing agent. For example, as the resin curing accelerator for the acid anhydride curing agent, for example, boron trifluoride monoethylamine, 2-ethyl-4-methylimidazole, 1-isobutyl-2-methylimidazole, 2-phenyl-4-methylimidazole Is mentioned. If it is a hardening accelerator for epoxy resins, it will be used at a content of 0.1 to 5 parts by mass with respect to 100 parts by mass of the epoxy resin.

  A solvent can be used from a viewpoint of reducing the viscosity of a heat conductive resin composition. As the solvent, a solvent that dissolves the resin from among known solvents is used. Examples of such solvents include methyl ethyl ketone, acetone, cyclohexanone, toluene, xylene, monochlorobenzene, dichlorobenzene, trichlorobenzene, phenol, and hexafluoroisopropanol. A solvent is used by content of 0-10,000 mass parts with respect to 100 mass parts of resin.

  The heat conductive resin composition that is the object of this evaluation method can be obtained by uniformly mixing the boron nitride powder, the resin, and if necessary, further components by stirring or kneading. For example, when the resin is a thermoplastic resin, the material is kneaded at a temperature equal to or higher than the melting temperature of the thermoplastic resin using a general kneading machine such as a single-screw or twin-screw kneader. The heat conductive resin composition used for this evaluation method can be obtained.

  For example, when the resin is a thermosetting resin such as an epoxy resin or a silicone resin, by uniformly mixing the boron nitride powder and the resin before curing, or by curing the resulting mixture The heat conductive resin composition which is the object of this evaluation method can be obtained.

  The molded object that is the subject of this evaluation method is formed by molding the thermally conductive resin composition of the present evaluation method. Molding of the molded body can be appropriately performed according to the state of the heat conductive resin composition and the type of resin by using a method generally used for molding of the resin composition.

  For example, the molding of the molded body using the heat conductive resin composition having plasticity and fluidity can be performed by curing the heat conductive resin composition in a desired shape, for example, in a state of being accommodated in a mold. In the production of such a molded body, injection molding, injection compression molding, extrusion molding, and compression molding can be used.

In the case where the resin is a thermosetting resin such as an epoxy resin or a silicone resin, the molding of the molded body, that is, curing can be performed under each curing temperature condition. When the resin is a thermoplastic resin, the molded body can be molded under conditions of a temperature equal to or higher than the melting temperature of the thermoplastic resin and a predetermined molding speed and pressure.
Moreover, the said molded object can be obtained by cutting out the hardened | cured material of a heat conductive resin composition in a desired shape.

[1-4. Measurement of distribution information on heat conduction by thermophysical microscope]
The measurement of the distribution information regarding the heat conduction of the heat conductive composition by the thermophysical microscope used in this evaluation method will be described.

  FIG. 2 is a schematic diagram showing the configuration of an example of a thermophysical microscope used in this evaluation method. The measurement sample 201 is placed on the XY stage 221 so that the laser beam irradiation and condensing position can be adjusted. The measurement sample 201 on the XY stage 221 is irradiated with laser light from the heating laser 211 and the detection laser 212, respectively.

  The heating laser 211 is, for example, an 830 nm wavelength laser diode, and performs heating with a laser beam of an 80 μm beam spot that is amplitude-modulated at a frequency of 999 KHz generated from the function generator 222. The detection laser 212 is, for example, a 782 nm wavelength laser diode, and measures thermal fluctuations with a reference laser beam having a 22 μm beam spot.

  Beam splitters 223 and 224 are provided on the optical axis of the detection laser 212. The laser beams reflected by the beam splitters 223 and 224 are guided to the detector 225 and the CCD camera 226, respectively.

The detector 225 detects the laser beam of the detection laser. For example, a silicon photodiode can be used as the detector 225. By comparing the amplitude waveform of the thermal fluctuation measured by the detector 225 with the waveform generated by the function generator 222, a period shift (phase difference) of the thermal fluctuation is obtained.
The CCD camera 226 adjusts the irradiation of the heating laser 211 and the detection laser 212.

  A metal film of one or more alloys of molybdenum, aluminum, gold, nickel, platinum, tungsten, tantalum, chromium, and iron is attached on the sample to be measured, and this metal film is cycled from the heating laser 211. Heating is performed by irradiating a laser beam for heating whose intensity is changed. By utilizing the fact that the reflectance of light from the object surface changes with temperature, the temperature change of the metal film surface is detected (periodic thermoreflectance method). The reflectance is measured from a change in the intensity of the reflected light of another detection laser beam 212 irradiated on the metal film. If the sample easily conducts heat, the heating cycle and the temperature change cycle coincide. On the other hand, in the sample that is difficult to transfer heat, the two periods are shifted. The ease of heat transfer (thermal diffusivity) of the sample can be determined from the amount of deviation.

The surface of a semi-infinite sample with thermal diffusivity α is cyclically heated, its temperature θ is period f, and amplitude θ ₀ θ (t) = θ ₀ sin (2πt / f)
The temperature response at the position x from the surface is
θ _x (t) = θ ₀ exp (−x (πt / (αf)) ^1/2・ sin (2πt / (f−x (πt / (αf)) ^1/2 ))
It is represented by If the phase difference at position x is φ _x ,
α = (πx ² ) / [f (ln (θ _x / θ ₀ )) ² ] = (πx ² ) / (fφ _x ² )
Thus, the thermal diffusivity α can be obtained by experimentally measuring the amplitude θ _x or the phase difference φ _x .

  If the laser beam is narrowed and irradiated, a small range of heat transfer properties (thermal diffusivity) can be obtained (thermophysical microscope), and if the measurement position is changed little by little using the XY stage 221, A two-dimensional distribution of heat transfer properties (thermal diffusivity) can be obtained.

The thermal conductivity can be obtained by multiplying the thermal diffusivity by the specific heat and density of the substance. Moreover, the heat permeability can be obtained by taking the square root of the value obtained by multiplying the thermal conductivity, the specific heat, and the density.
As distribution information related to heat conduction, any of thermal diffusivity, thermal conductivity, or thermal permeability may be used.

[1-5.2 Analysis method of distribution information on heat conduction by two-dimensional DFA]
As an analysis method for obtaining fractal information from distribution information related to heat conduction, a two-dimensional DFA (Drenched Fractation Analysis) method will be described.

  With 2D DFA, 2D data is divided by a specific scale (square window size), the magnitude of fluctuation after subtracting the trend in each square window is obtained, and the correlation between the magnitude of fluctuation and the scale is calculated. This is an analysis method for log-log plotting. The trend is a trend in which m-order polynomials (natural numbers such as m = 0, 1, 2,...) Are applied to the two-dimensional data in each window by the method of least squares. The method will be described in detail below.

(1) First, two-dimensional data x (i, j) consisting of M × N values (M and N represent natural numbers), i = 1, 2,... M, j = 1, 2,. _.. , N, and the average value x _avr minus the total are integrated to obtain new time series data y (k, l), k = 1, 2,... M, l = 1, 2,. -Create N. The average value x _avr and the time series data y (k, l) are described as in the following formulas 1 and 2.

(2) The above-mentioned two-dimensional data y (k, l) is divided into two-dimensional areas of scale (square window size) s × s. That is, the two-dimensional data y (k, l) is divided by a square window made up of s × s (s represents a natural number) data.

(3) An m-order polynomial (m is a natural number such as 0, 1, 2,..., Typically m = 2 is used) for the two-dimensional data y (k, l) in each window. Is applied by the least square method to obtain the trend y _s (k, l) in each window.
(4) The fluctuation magnitude FL (s) after subtracting the trend y _s (k, l) from the two-dimensional data y (k, l) is obtained according to the following equation 3.

(5) Fluctuation magnitude FL (s) when the window size s is changed is obtained, logarithm is obtained for the product s × s of the window size s and the fluctuation magnitude FL (s), and log ₁₀ The relationship between (s × s) and log ₁₀ FL (s) is plotted on a graph. This plot is called a two-dimensional DFA plot. If a straight line can be applied to this two-dimensional DFA plot (linear approximation), the inclination of the straight line (the inclination of the two-dimensional DFA) becomes the fractal index of the original two-dimensional data x (i, j). The slope of this two-dimensional DFA can be used as fractal information about the thermally conductive composition.

  If the original two-dimensional data is constant, constantly increasing or decreasing, or has periodicity, the two-dimensional DFA plot is a straight line with a two-dimensional DFA slope of 0. It is known that if it is pink noise, it will be a straight line with a slope of 1.0 if it is pink noise, and if it is brown noise it will be a straight line with a slope of 1.5.

In general, in the case of two-dimensional composite surface data, the two-dimensional DFA plot does not become a perfect straight line. Therefore, in order to obtain the local inclination of the two-dimensional DFA accompanying the change in the window size s, _{log 10} and (s × s) of the logarithm for the product s × s, to perform the differential plot for plotting (2D DFA plot) of _log and 10 FL (s) of the logarithm for the fluctuation component FL (s) A differential plot of a two-dimensional DFA plot is created. The differential plot of the two-dimensional DFA plot is obtained as a plot representing the relationship between log ₁₀ (s × s) based on the window size s and the slope of the two-dimensional DFA. Based on the differential plot of the two-dimensional DFA plot, the slope of the two-dimensional DFA in a specific window size s can be obtained. That is, the differential plot of the two-dimensional DFA plot represents the relationship between the window size s used for analysis and the slope of the two-dimensional DFA.

[1-6. Analysis method of distribution information on heat conduction by box counting method]
Next, a box counting method will be described as an analysis method for obtaining fractal information from distribution information related to heat conduction.

The box counting method is a method for obtaining a fractal dimension of a curve in a plane, taking a two-dimensional case as an example. The plane is divided by a square grid (box) of length L on one side, and the number N (L) of square boxes containing the curve in each divided square box is obtained, and the length of one side of the box If the following relational expression may be approximated when D is changed, D becomes a fractal dimension. When the box counting method is applied to three dimensions, a curve is a curve or a curved surface, a plane is a three-dimensional space, and a square is obtained as a cube.
N (L) = C x L ^-D (C is a constant coefficient)

  Other methods for obtaining the fractal dimension include a scale conversion method, a cover method, a visual field expansion method, a turning radius method, and a density correlation function method. These methods may be used.

  In this case, in order to obtain a curve to which the box counting method is applied from a distribution image of thermal conductivity, thermal diffusivity, or thermal permeability, binarization is performed, and the place where the value changes is defined as a curve. The binarization is preferably performed with respect to the thermal conductivity, thermal diffusivity, and thermal permeability of the resin of the thermally conductive resin composition and the others. In addition, the threshold is set to a P-tile method using a density histogram, a mode method, a discriminant analysis method, a minimum error method, a differential histogram method using a local property, and a distribution image having as few “crushed” and “blurred” as possible. To correct the density value of the pixel of interest, the distribution image was divided into several small areas, the density histogram was examined for each small area, and it was determined that there was a part where the value changed greatly there In this case, a dynamic threshold processing method that sets a threshold that best suits the properties of the small region, a pixel of interest is predicted using a joint probability between a pixel of a binarized distribution image and a pixel of interest, which is another example, and prediction If the probability that the value is 1 is large, a value obtained by subtracting a certain amount from a certain threshold value so that the pixel of interest after binarization is likely to be 1 is set as a new threshold value, and it is highly likely that the predicted value is 0. Nara threshold It may be applied adaptive processing method to a value obtained by adding a predetermined amount to a new threshold.

  The fractal dimension calculated | required by said method can be used as fractal information about a heat conductive composition.

[1-7. Thermal conductivity evaluation method]
Based on the fractal information obtained from two-dimensional thermal property distribution data obtained from a thermophysical microscope, the fractal property and structure formation of the thermally conductive composition are judged and the thermal conductivity is evaluated. As information, the case where thermal conductivity is evaluated based on the inclination of the two-dimensional DFA obtained by the two-dimensional DFA method will be described.

Depending on the slope value of the two-dimensional DFA, the fractal nature of the original two-dimensional data can be evaluated as follows.
When the two-dimensional DFA has a straight line with an inclination of 0.5, that is, white noise, it is a complete random number, indicating that the original two-dimensional data has no structure.

  In the case of a straight line with a two-dimensional DFA slope of 1.0, that is, pink noise, the frequency component characteristic in which the intensity at the frequency is inversely proportional to the frequency is shown. Since light having the same frequency component appears pink, it is called pink noise and is also called 1 / f fluctuation. In this case, it shows that the original two-dimensional data has a fractal structure.

When the two-dimensional DFA has a straight line with an inclination of 1.5, that is, brown noise, the frequency component is generated by Brownian motion (Brownian motion) and the intensity at the frequency is inversely proportional to the square of the frequency. The characteristics are shown. In this case, it shows that the original two-dimensional data has a fractal structure different from pink noise.
When the two-dimensional DFA plot is a straight line, the slope of the straight line can be used as the slope of the two-dimensional DFA.

  On the other hand, when the two-dimensional DFA plot does not become a straight line, the slope of the two-dimensional DFA in a specific window size s can be used from the differential plot of the two-dimensional DFA plot.

  For example, when the slope of the two-dimensional DFA is larger than 0.5 in a specific window size s, it indicates that the two-dimensional data has a fractal structure in the vicinity of the corresponding window size s.

  On the other hand, for example, when the two-dimensional data has a structure having some periodicity, the two-dimensional DFA differential plot has a local decrease in the slope of the two-dimensional DFA in the vicinity of the window size s corresponding to the period, It approaches 0.

In order to determine that the distribution information regarding heat conduction has fractal properties, that is, the heat conductive composition has fractal properties, when log ₁₀ (s × s) takes a specific value, the slope of the two-dimensional DFA is It may be larger than 0.5. That is, if the slope of the two-dimensional DFA is larger than 0.5 in a specific window size s, it can be determined that the fractal structure is provided.

  This is because when two-dimensional DFA analysis is performed on two-dimensional data, whether or not a fractal structure appears depends on the window size of the two-dimensional DFA analysis. If the two-dimensional data has a fractal structure, a pattern with fractal characteristics may appear strongly in a square window at a specific window size. If the window size is too large or too small, it is included in the square window. The fractal nature may be weakened. Even in such a case, if a fractal structure appears in a specific window size, it can be determined that the two-dimensional data has a fractal structure. For this reason, if it can be determined that the two-dimensional DFA has a fractal structure from the inclination of the two-dimensional DFA in a specific window size, the target two-dimensional data is determined to have a fractal structure.

  As the inclination or fractal dimension of the two-dimensional DFA takes a value larger than 0.5, the secondary particles formed by aggregating the primary particles (for example, heat conductive particles) of the components constituting the heat conductive composition, It can be predicted that the fractal structure is aligned. For this reason, in the case of having a fractal structure, it is considered that a structure that forms a heat conductive path is formed due to the connected state of the secondary particles, and therefore it can be evaluated that the heat conductivity is high. .

  On the other hand, when the inclination of the two-dimensional DFA takes a value near 0.5, the primary particles are randomly arranged, and it is predicted that a structure that contributes to thermal conductivity by the secondary particles is not constructed. For this reason, in this case, it can be evaluated that the thermal conductivity is low.

  Further, when the inclination of the two-dimensional DFA takes a value near 0, the primary particles are periodically arranged, and it is predicted that a structure that contributes to thermal conductivity by the secondary particles is not constructed. For this reason, in this case, it can be evaluated that the thermal conductivity is low.

<First embodiment>
Next, as embodiments of the present invention, a thermal conductivity evaluation apparatus according to a thermal conductivity evaluation method, a program for causing a computer to execute the thermal conductivity evaluation method, and a computer-readable recording medium storing the program Will be described.

[2. Thermal conductivity evaluation device]
[2-1. Configuration example of thermal conductivity evaluation apparatus]
FIG. 1 is a diagram schematically showing a hardware configuration of a thermal conductivity evaluation device 1 according to the present invention. FIG. 2 is a diagram schematically showing functional blocks of the thermal conductivity evaluation device 1 of the present invention.

  As shown in FIG. 1, the thermal conductivity evaluation apparatus 1 includes an information processing apparatus 20, a thermophysical microscope 11, an external memory 12, a keyboard 13, a printer 14, and a display 15. The information processing apparatus 20 is a computer that includes an input interface 21, a bus 22, a CPU (Central Processing Unit) 23, a memory 24, and an output interface 25.

  The thermophysical microscope 11 measures distribution information related to heat conduction of a target sample, and inputs distribution information (data) related to heat conduction to the information processing apparatus 20 via the input interface 21. .

  The external memory 12 is connected to the input interface, and can read distribution information related to heat conduction or write distribution information related to heat conduction from the information processing apparatus 20 to the information processing apparatus 20. The external memory 12 can also record computer software for acquiring fractal information and computer software for evaluating thermal conductivity. In this case, the computer software can be read from the memory and downloaded to the information storage device as necessary.

  The keyboard 13 is an information input device connected to the input interface 21, and the operator operates the keyboard 13 to operate the information processing device 20 and the thermal conductivity evaluation device 1.

  The input interface 21 is a unit that exchanges information between the information processing apparatus 20 and the outside. The units 11 to 13 are connected as described above, and when information (signals) is received from the units 11 to 13, the information is transmitted via the bus 22. Thus, signals are appropriately transmitted to the units 23 to 24 in the information processing apparatus 20.

  The CPU 23 is a processing device that performs various controls and operations, and implements various functions by executing fractal information acquisition computer software and thermal conductivity evaluation computer software stored in the memory 24. To do. And CPU23 functions as the fractal information acquisition means 31 mentioned later in FIG. 2, and the heat conductivity evaluation means 32 by running these computer programs.

  A program (fractal information acquisition computer software, thermal conductivity evaluation computer software) for realizing the functions as the fractal information acquisition unit 31 and the thermal conductivity evaluation unit 32 is, for example, a flexible disk, a CD (CD -ROM, CD-R, CD-RW, etc.), DVD (DVD-ROM, DVD-RAM, DVD-R, DVD + R, DVD-RW, DVD + RW, HD DVD, etc.), Blu-ray disc, magnetic disc, optical disc, magneto-optical It is provided in a form recorded on a computer-readable recording medium (for example, the external memory 12) such as a disk. The information processing apparatus 20 reads the program from the recording medium, transfers it to an internal storage device (for example, the memory 24) or an external storage device, and uses it. The program is recorded in a storage device (recording medium) (not shown) such as a magnetic disk, an optical disk, or a magneto-optical disk, and is provided from the storage device to the information processing apparatus 20 via a communication path. Also good.

  When realizing the functions as the fractal information acquisition means 31 and the thermal conductivity evaluation means 32, the program stored in the internal storage device (memory 24 in this embodiment) is stored in the microprocessor of the information processing device 20 (this embodiment). Then, it is executed by the CPU 23). At this time, the computer may read and execute a program recorded in an external recording medium (for example, the external memory 12).

Here, the computer software for obtaining fractal information is obtained by obtaining fractal information about a heat conductive composition by obtaining distribution information related to heat conduction with the thermophysical microscope 11 and analyzing the distribution information related to heat conduction. The computer software for evaluating thermal conductivity is for evaluating the thermal conductivity of the thermal conductive composition based on fractal information.

  The computer software for obtaining fractal information and the computer software for evaluating thermal conductivity are stored in the various computer-readable recording media.

  In the present embodiment, the computer is a concept including hardware and an operating system, and means hardware that operates under the control of the operating system. Further, when an operating system is unnecessary and hardware is operated by an application program alone, the hardware itself corresponds to a computer. The hardware includes at least a microprocessor such as a CPU and means for reading a computer program recorded on a recording medium.

  The memory 24 is a storage unit that stores various data and programs, and is realized by, for example, a volatile memory such as a RAM (Random Access Memory) or a nonvolatile memory such as a ROM or a flash memory. In the present embodiment, the memory 24 stores fractal information acquisition computer software, thermal conductivity evaluation computer software, fractal information, and thermal conductivity evaluation data to be executed by the CPU 23.

  The output interface 25 is a unit that exchanges information between the information processing apparatus 20 and the outside. The units 14 and 15 are connected to each other, and information (signals) is transmitted from the respective sections 21, 23, and 24 in the information processing apparatus 20 via the bus 22. ) Is transmitted to the units 14 and 15.

  The printer 14 and the display 15 are connected to an output interface 25, and print or display information obtained by the processing of the CPU 23 to the operator. Of course, the printer 14 and the display 15 also include a drive circuit (driver) for printing or display.

[2-2. Functional configuration of thermal conductivity evaluation device]
Next, the functional configuration of the thermal conductivity evaluation device will be described.
FIG. 3 is a diagram schematically illustrating a functional block of a thermal conductivity evaluation apparatus as an example of an embodiment.

  When the thermal conductivity evaluation device 30 is functionally represented, as shown in FIG. 3, the thermal conductivity evaluation device 30 includes a fractal information acquisition unit 31 as a first unit and a thermal conductivity evaluation unit as a second unit. 32. The fractal information acquisition unit 31 and the thermal conductivity evaluation unit 32 execute software by a computer program, so that the software functions as the fractal information acquisition unit 31 and the thermal conductivity evaluation unit 32. . This software is stored in the memory 24 and is read and executed by the CPU 23.

  The fractal information acquisition means 31 obtains fractal information about the heat conductive composition by analyzing the distribution information related to heat conduction measured by the thermophysical microscope 11 using the above-described fractal information acquisition computer software. .

  The thermal conductivity evaluation means 32 evaluates the thermal conductivity of the thermal conductive composition on the fractal information obtained by the fractal information acquisition means 31 using the above-described computer software for thermal conductivity evaluation.

  As described above, the analysis of distribution information related to heat conduction by the fractal information acquisition unit 31 can use a known method of obtaining fractal information from two-dimensional information, but uses a two-dimensional DFA method or a box counting method. It is preferable.

[2-3. Operation of thermal conductivity evaluation apparatus and thermal conductivity evaluation method using thermal conductivity evaluation apparatus]
The operation of the thermal conductivity evaluation apparatus as an example of the embodiment will be described according to the flowchart shown in FIG.
FIG. 4 is a flowchart for explaining the thermal conductivity evaluation process in the thermal conductivity evaluation apparatus as an example of the embodiment.

First, distribution information relating to heat conduction of the heat conductive composition is measured by the thermophysical microscope 11 (step S11).
When data of distribution information related to heat conduction of the heat conductive composition is input to the fractal information acquisition unit 31, the fractal information acquisition unit 31 uses the fractal information acquisition computer software to calculate the heat conductive composition. The fractal information is output to the thermal conductivity evaluation means 32 (step S12).

  The thermal conductivity evaluation unit 32 evaluates the thermal conductivity of the thermal conductive composition using the thermal conductivity evaluation computer software based on the fractal information input from the fractal information acquisition unit 31 (step S13).

  In step S11, the distribution information regarding the heat conduction of the heat conductive composition may be input along with the data measured by the thermophysical microscope 11, and the data measured in advance is stored in the external memory 12. It is also possible to read and input from.

  Examples of the present invention will be described below, but the scope of the present invention is not limited to the following examples unless it exceeds the gist.

<Example>
An embodiment of the present invention will be described with reference to the flowchart shown in FIG.
Evaluation of thermal conductivity according to this example is performed as follows.

  First, a measurement sample of a molded body of a heat conductive composition, which is a target of thermal conductivity evaluation, is created (step S21). Next, a two-dimensional thermophysical property distribution indicating distribution information related to the heat conduction of the measurement sample of the thermally conductive resin molded body is measured by a thermophysical microscope (step S22). Furthermore, the inclination of the two-dimensional DFA is obtained by analyzing the two-dimensional thermophysical property distribution (step S23). From the inclination of the two-dimensional DFA, by evaluating the fractal property, it is determined whether the aggregated particles contained in the thermally conductive resin molded body form a structure (step S24). Then, the thermal conductivity distribution is evaluated from the structure of the aggregated particles (step S25).

(Preparation of heat conductive composition)
In this example, the materials used for preparing the heat conductive composition are shown below.
Bisphenol epoxy resin (main agent) (a): jER828 manufactured by Mitsubishi Chemical Corporation
Epoxy resin curing agent (acid anhydride type) (b): jER Cure YH300 manufactured by Mitsubishi Chemical Corporation
Curing accelerator (b): jER Cure EMI24 manufactured by Mitsubishi Chemical Corporation
-Powder A: Boron nitride PT670 manufactured by Momentive Performance Materials

   80 parts by mass of the epoxy resin curing agent (b) is blended with 100 parts by mass of the bisphenol-based epoxy resin (a), and 2 minutes at 2,000 rpm using a rotation / revolution vacuum mixer ARV-310 manufactured by Sinky Corporation. After mixing, 2 parts by mass of the curing accelerator (c) was added and mixed at 2,000 rpm for 2 minutes using the same mixer to obtain an epoxy mixture.

  Powder A was added to the resulting epoxy mixture so that the volume percentage of the powder was 90%, and the mixture was mixed for 5 minutes with an automatic mortar ANM-150 manufactured by Nichito Kagaku Co., Ltd. The thing 101 was obtained. As shown in FIG. 6A, the thermally conductive resin composition 101 is placed in a mold (press frame 102), and the press frame 102 is sandwiched by a press plate 104 through a Teflon (registered trademark) sheet 103, and the temperature At 150 ° C. and a press pressure of 15 MPa, pressure was applied in the direction indicated by the arrow in the drawing, followed by pressure curing for 1 hour.

  The measurement sample which cut out this in the surface direction and the thickness direction was created. As a measurement sample in the surface direction, as shown in FIG. 6B, an intermediate sample 111 cut out in the surface direction from the heat conductive molded product in the thickness direction was used. As a measurement sample in the thickness direction, as shown in FIG. 6C, an intermediate sample 112 cut out in the thickness direction from the heat conductive molded product in the surface direction was used.

(Measurement of distribution information on heat conduction by thermophysical microscope)
A molybdenum metal film was deposited on the measurement sample by vacuum deposition. The metal film of the measurement sample was heated with a laser beam of an 80 μm beam spot of an 830 nm wavelength laser diode having a modulation period of 999 KHz. Thermal fluctuations were measured with a 22 μm reference laser beam from a 782 nm wavelength laser diode. An XY stage is provided in the thermophysical microscope, and the two-dimensional surface of each measurement sample cut out in the surface direction and thickness direction is measured at 20μ intervals, and the two-dimensional thermophysical distribution is determined from the phase difference with the modulation period. A thermal diffusivity distribution is obtained.

  The thermal diffusivity distribution of the 1200 μm square region of the measurement sample cut out in the surface direction is shown in FIG. 7 (a-1), and the thermal diffusivity distribution of the 1200 μm square region of the measurement sample cut out in the thickness direction is shown in FIG. 2).

(Analysis of distribution information on heat conduction by two-dimensional DFA)
Two two-dimensional thermal diffusivity distributions in the surface direction and the thickness direction were analyzed by two-dimensional DFA.
The fluctuation size FL (s) of the thermal diffusivity when the window size s was changed was obtained, and a two-dimensional DFA plot of log ₁₀ (s × s) and log ₁₀ FL (s) was obtained. Further, a differential plot of the two-dimensional DFA plot was created, and the slope of the two-dimensional DFA plot accompanying the change in the window size s was obtained. The result of the two-dimensional DFA analysis of the thermal diffusivity of the measurement sample cut out in the surface direction (differential plot of the two-dimensional DFA plot) is shown in FIG. FIG. 7B-2 shows the result of the two-dimensional DFA analysis (differential plot of the two-dimensional DFA plot).

  The inclination of the two-dimensional DFA of the measurement sample cut out in the surface direction was approximately in the vicinity of 0.6. On the other hand, the slope of the two-dimensional DFA of the measurement sample cut out in the thickness direction increased from around 0.6 as the window size increased, and there was a scale that partially took a value close to 1.

(Evaluation of thermal conductivity)
In the surface direction, the two-dimensional DFA slope is in the vicinity of 0.6 regardless of the change in the window size, so it is determined that the two-dimensional thermal diffusivity distribution in the surface direction has fractal characteristics. Can do. However, since the inclination of the two-dimensional DFA is close to 0.5, the molded body of the heat conductive composition has a fractal structure in the surface direction, but the primary particles tend to be randomly arranged, and the heat conductivity It can be determined that it does not have a clear structure that forms the path. Therefore, it can be evaluated that the molded body of the heat conductive composition of this example has a low thermal conductivity in the surface direction.

  On the other hand, in the thickness direction, the value of the logarithm of the product of window sizes (s × s) is around 2.7, and the value of the slope of the two-dimensional DFA is close to 1. From this, it can be determined that the two-dimensional thermal diffusivity distribution in the thickness direction has fractal properties in the window size. For this reason, the molded body of the heat conductive composition has a fractal structure with secondary particles larger than the primary particles in the thickness direction and forms a heat conductive path due to the connected state of the secondary particles. Can be determined. Therefore, it can be evaluated that the molded body of the heat conductive composition of this example has higher heat conductivity in the thickness direction than in the surface direction.

  In this example, as shown in FIG. 8, the heat conduction molded product 110 used in this example is obtained from the distribution information regarding the heat conduction of the measurement sample 111 cut out in the surface direction. The thermal conductivity in the upper and lower broken lines in the figure is evaluated, and from the distribution information regarding the thermal conduction of the measurement sample 112 cut out in the thickness direction, the thickness of the heat conduction molded product 110 (both broken lines on the left and right in the figure) By evaluating the thermal conductivity in the direction of the arrow, it is evaluated that the thermal conductivity in the direction perpendicular to the press direction (thickness direction) is superior to the thermal conductivity in the press direction.

(Measurement of thermal conductivity by laser flash method)
About each measurement sample which cut out said heat conductive molded article in the surface direction and thickness direction, the heat conductivity was measured by the laser flash method. The heat conductivity in the surface direction and the thickness direction was measured by applying heat to the measurement sample with a laser and increasing the temperature with a thermocouple. Measurement results of 50 W / (m · K) in the surface direction and 40 W / (m · K) in the thickness direction were obtained, and it was found that the thermal conductivity in the thickness direction was high.
This result shows the same result as the evaluation of the thermal conductivity evaluation method of the present invention that the thermal conductivity in the thickness direction considered to have a fractal structure is high.

  However, measurement by the laser flash method cannot measure a minute range, and the thermal conductivity distribution near the surface of the measurement surface is obtained. In addition, it is impossible to evaluate the percolation state of a highly thermally conductive composition that is considered to affect the thermal conductivity.

[Others]
In the above embodiments and examples, the fractal information acquisition and the thermal conductivity evaluation are performed using desired computer software. However, as described above, this evaluation method is manually performed without using a computer. Of course, it can be realized by a method.

  This evaluation method evaluates the structure of a molded body of a thermally conductive resin composition that has been developed with each concept, for example, contributes to improvement of thermal conductivity, and gives a measure of improvement. . This can contribute to, for example, optimization of the manufacturing condition factor of the composition of the heat conductive composite material and the molded body, and optimization of the secondary processing condition factor. In addition, the failure rate can be reduced and the productivity can be improved by feeding back the on-line analysis during manufacturing and processing. This evaluation method is also expected to bring about further development in the technical field using such a molded body, for example, a heat radiating material of an electronic device.

1 Thermal conductivity evaluation device 1
DESCRIPTION OF SYMBOLS 11 Thermophysical microscope 12 External memory 13 Keyboard 14 Printer 15 Display 20 Information processing apparatus 21 Input interface 22 Bus 23 CPU (Central Processing Unit)
24 memory 25 output interface 30 thermal conductivity evaluation device functional 31 fractal information acquisition means (first means)
32 Thermal conductivity evaluation means (second means)
101 Thermally conductive resin composition 102 Press frame (mold)
103 Teflon (Registered Trademark) Sheet 104 Press Plate 110 Thermal Conductive Molded Product 111 Surface Direction Measurement Sample 112 Thickness Direction Measurement Sample 201 Measurement Sample 211 Heating Laser 212 Detection Laser 221 XY Stage 222 Function Generator 223, 224 Beam Splitter 225 Detector 226 CCD camera

Claims (14)

A first step of obtaining a fractal information about the heat conductive composition by performing a required arithmetic processing on the distribution information regarding the heat conduction about the heat conductive composition;
And a second step of evaluating the thermal conductivity of the thermally conductive composition based on the fractal information obtained in the first step. Evaluation methods.   The thermal conductivity evaluation of the thermally conductive composition according to claim 1, wherein the first step is a step of obtaining the fractal information by analyzing the distribution information by a two-dimensional DFA method. Method.   3. The thermal conductivity evaluation method for a thermal conductive composition according to claim 2, wherein the fractal information is a two-dimensional DFA plot or a slope of a two-dimensional DFA obtained from a differential plot of the two-dimensional DFA plot. .   The second step evaluates the fractal property of the thermally conductive composition from the slope of the two-dimensional DFA obtained by the two-dimensional DFA method, and based on the evaluation result, heat of the thermally conductive composition is evaluated. The method for evaluating thermal conductivity of a thermally conductive composition according to claim 3, wherein the method is a step of evaluating conductivity.   2. The thermal conductivity evaluation method for a thermal conductive composition according to claim 1, wherein the first step is a step of obtaining the fractal information by analyzing the distribution information by a box counting method. .   6. The thermal conductivity evaluation method for a thermal conductive composition according to claim 5, wherein the fractal information is a fractal dimension.   In the second step, the fractal property of the thermally conductive composition is evaluated from the fractal dimension obtained by the box counting method. Based on the evaluation result, the thermal conductivity of the thermally conductive composition is determined. It is a step to evaluate, The thermal conductivity evaluation method of the heat conductive composition of Claim 5 or Claim 6 characterized by the above-mentioned.   The method for evaluating thermal conductivity of a thermally conductive composition according to any one of claims 1 to 7, wherein the thermally conductive composition is a thermally conductive resin composition.   9. The distribution information related to the heat conduction is any one of heat diffusivity distribution information, heat conductivity distribution information, and heat permeability distribution information. The thermal-conductivity evaluation method of the heat conductive composition of description. A first means for obtaining a slope of a two-dimensional DFA by analyzing distribution information relating to heat conduction of the thermally conductive composition by a two-dimensional DFA method;
A second method for evaluating the fractal property of the thermally conductive composition from the inclination of the two-dimensional DFA obtained by the first means and evaluating the thermal conductivity of the thermally conductive composition based on the evaluation result. And a thermal conductivity evaluation apparatus for the thermal conductive composition.   The thermal conductivity evaluation apparatus for a thermal conductive composition according to claim 10, wherein the thermal conductive composition is a thermal conductive resin composition.   The distribution information on the heat conduction is distribution information of any one of thermal diffusivity distribution information, thermal conductivity distribution information, and thermal permeability distribution information. The thermal conductivity evaluation apparatus of the thermal conductive composition. Computer
A first means for obtaining a slope of a two-dimensional DFA by analyzing distribution information relating to heat conduction of the thermally conductive composition by a two-dimensional DFA method;
A second method for evaluating the fractal property of the thermally conductive composition from the inclination of the two-dimensional DFA obtained by the first means and evaluating the thermal conductivity of the thermally conductive composition based on the evaluation result. A computer-readable recording medium having recorded thereon a program for functioning as a means. Computer
A first means for obtaining a slope of a two-dimensional DFA by analyzing distribution information relating to heat conduction of the thermally conductive composition by a two-dimensional DFA method;
A second method for evaluating the fractal property of the thermally conductive composition from the inclination of the two-dimensional DFA obtained by the first means and evaluating the thermal conductivity of the thermally conductive composition based on the evaluation result. Program to function as a means.