1. A polypropylene resin pre-expanded particle comprising as a base resin a polypropylene resin composition containing:
(A) a polypropylene resin and
(B) a compound having a triazine skeleton and molecular weight of at most 300 per triazine skeleton unit.
2. The polypropylene resin pre-expanded particle of claim 1, which further comprises hydrophilic polymer (C).
3. The polypropylene resin pre-expanded particle of claim 1 or 2, wherein said compound (B) having a triazine skeleton and molecular weight of at most 300 per triazine skeleton unit is contained in an amount of 0.05 to 6 parts by weight based on 100 parts by weight of said polypropylene resin (A).
4. The polypropylene resin pre-expanded particle of claim 2, wherein said hydrophilic polymer (C) is contained in an amount of 0.01 to 20 parts by weight based on 100 parts by weight of said polypropylene resin (A).
5. The polypropylene resin pre-expanded particle of claim 1 or 2, which further comprises inorganic filler (D).
6. The polypropylene resin pre-expanded particle of claim 5, wherein said inorganic filler (D) is contained in an amount of 0.005 to 10 parts by weight based on 100 parts by weight of said polypropylene resin (A).
7. The polypropylene resin pre-expanded particle of claim 1 or 2, wherein said polypropylene resin (A) is at least one member selected from the group consisting of ethylene-propylene random copolymer, propylene-butene-1 random copolymer and ethylene-propylene-butene-1 random copolymer.
8. The polypropylene resin pre-expanded particle of claim 1 or 2, wherein said compound (B) having a triazine skeleton and molecular weight of at most 300 per triazine skeleton unit is at least one member selected from the group consisting of melamine, isocyanuric acid and melamine isocyanurate.
9. The polypropylene resin pre-expanded particle of claim 2, wherein said hydrophilic polymer (C) is an ethylene ionomer resin obtained by crosslinking an ethylene-(meth)acrylic acid copolymer with an alkali metal ion.
10. The polypropylene resin pre-expanded particle of claim 1 or 2, which has two melting peaks in the DSC curve obtained by differential scanning calorimetry.
11. An in-mold expanded article comprising the polypropylene resin pre-expanded particle of claim 1 or 2.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A semiconductor failure analysis apparatus for performing a failure analysis by means of a thermal image of a semiconductor device, comprising:
voltage applying means applying a bias voltage to a semiconductor device to serve as an analysis object;
imaging means acquiring an image of the semiconductor device; and
image processing means performing image processing necessary for a failure analysis of the semiconductor device for an image acquired by the imaging means, wherein
the imaging means acquires a plurality of analysis images each including a thermal image in a state where the bias voltage is applied to the semiconductor device and a plurality of background images in a state where the bias voltage is not applied thereto, and
the image processing means includes:
imaging position calculating means calculating, for each of the plurality of analysis images and the plurality of background images, an imaging position thereof;
image classifying means preparing, for the imaging position of each of the plurality of analysis images and the plurality of background images, a region division unit set with reference to a position frequency distribution of the imaging positions, and classifying the plurality of analysis images and the plurality of background images into N image groups (N is an integer not less than 2) according to which of the N regions divided in accordance with the region division unit the imaging position belongs to; and
difference image generating means generating a difference image between the analysis image and the background image to be used for a failure analysis, individually for the classified N image groups.
2. The semiconductor failure analysis apparatus according to claim 1, wherein the image classifying means obtains the position frequency distribution of the imaging positions of each set of the plurality of analysis images and the plurality of background images, and sets the region division unit based on an average position \u03bc1 and a distribution width w1 in the position frequency distribution of the plurality of analysis images and an average position \u03bc2 and a distribution width w2 in the position frequency distribution of the plurality of background images.
3. The semiconductor failure analysis apparatus according to claim 2, wherein the image classifying means sets an adjustment factor \u03b1 to adjust the region division unit, determines a region unit \u03bc1\xb1\u03b1\xd7w1 for the plurality of analysis images and a region unit \u03bc2\xb1\u03b1\xd7w2 for the plurality of background images, and sets a common range of the region units as the region division unit.
4. The semiconductor failure analysis apparatus according to claim 3, wherein the image classifying means sets the adjustment factor \u03b1 based on a factor value input by an operator.
5. The semiconductor failure analysis apparatus according to claim 3, wherein the image classifying means determines each of the distribution width w1 in the position frequency distribution of the plurality of analysis images and the distribution width w2 in the position frequency distribution of the plurality of background images by a standard deviation \u03c3, and sets the adjustment factor \u03b1 within a range satisfying a condition of 1\u2266\u03b1\u22662.
6. The semiconductor failure analysis apparatus according to claim 1, wherein the difference image generating means performs, for the N difference images obtained respectively for the N image groups, weighting based on the numbers of images belonging to the respective image groups to thereby generate a difference image to be used for a failure analysis.
7. The semiconductor failure analysis apparatus according to claim 1, wherein the difference image generating means selects, from the N difference images obtained respectively for the N image groups, based on the numbers of images belonging to the respective image groups, a difference image to be used for a failure analysis.
8. The semiconductor failure analysis apparatus according to claim 1, wherein the difference image generating means is configured to be switchable between a first analysis mode of performing, for the N difference images obtained respectively for the N image groups, weighting based on the numbers of images belonging to the respective image groups to thereby generate a difference image to be used for a failure analysis, and a second analysis mode of selecting, based on the numbers of images belonging to the respective image groups, a difference image to be used for a failure analysis.
9. A semiconductor failure analysis method for performing a failure analysis by means of a thermal image of a semiconductor device, comprising:
a voltage applying step of applying a bias voltage to a semiconductor device to serve as an analysis object;
an imaging step of acquiring an image of the semiconductor device; and
an image processing step of performing image processing necessary for a failure analysis of the semiconductor device for an image acquired by the imaging step, wherein
the imaging step acquires a plurality of analysis images each including a thermal image in a state where the bias voltage is applied to the semiconductor device and a plurality of background images in a state where the bias voltage is not applied thereto, and
the image processing step includes:
an imaging position calculating step of calculating, for each of the plurality of analysis images and the plurality of background images, an imaging position thereof;
an image classifying step of preparing, for the imaging position of each of the plurality of analysis images and the plurality of background images, a region division unit set with reference to a position frequency distribution of the imaging positions, and classifying the plurality of analysis images and the plurality of background images into N image groups (N is an integer not less than 2) according to which of the N regions divided in accordance with the region division unit the imaging position belongs to; and
a difference image generating step of generating a difference image between the analysis image and the background image to be used for a failure analysis, individually for the classified N image groups.
10. The semiconductor failure analysis method according to claim 9, wherein the image classifying step obtains the position frequency distribution of the imaging positions of each set of the plurality of analysis images and the plurality of background images, and sets the region division unit based on an average position \u03bc1 and a distribution width w1 in the position frequency distribution of the plurality of analysis images and an average position \u03bc2 and a distribution width w2 in the position frequency distribution of the plurality of background images.
11. The semiconductor failure analysis method according to claim 10, wherein the image classifying step sets an adjustment factor \u03b1 to adjust the region division unit, determines a region unit \u03bc1\xb1\u03b1\xd7w1 for the plurality of analysis images and a region unit \u03bc2\xb1\u03b1\xd7w2 for the plurality of background images, and sets a common range of the region units as the region division unit.
12. The semiconductor failure analysis method according to claim 11, wherein the image classifying step sets the adjustment factor \u03b1 based on a factor value input by an operator.
13. The semiconductor failure analysis method according to claim 11, wherein the image classifying step determines each of the distribution width w1 in the position frequency distribution of the plurality of analysis images and the distribution width w2 in the position frequency distribution of the plurality of background images by a standard deviation \u03c3, and sets the adjustment factor \u03b1 within a range satisfying a condition of 1\u2266\u03b1\u22662.
14. The semiconductor failure analysis method according to claim 9, wherein the difference image generating step performs, for the N difference images obtained respectively for the N image groups, weighting based on the numbers of images belonging to the respective image groups to thereby generate a difference image to be used for a failure analysis.
15. The semiconductor failure analysis method according to claim 9, wherein the difference image generating step selects, from the N difference images obtained respectively for the N image groups, based on the numbers of images belonging to the respective image groups, a difference image to be used for a failure analysis.
16. The semiconductor failure analysis method according to claim 9, wherein the difference image generating step is configured to be switchable between a first analysis mode of performing, for the N difference images obtained respectively for the N image groups, weighting based on the numbers of images belonging to the respective image groups to thereby generate a difference image to be used for a failure analysis, and a second analysis mode of selecting, based on the numbers of images belonging to the respective image groups, a difference image to be used for a failure analysis.