- Email: [email protected]
Nonstoichiometry fluctuations along striations in undoped semi-insulating GaAs
Journal of Crystal Growth 126 (1993) 77—84 North-Holland
~
CRYSTAL GROWTH
Nonstoichiometry fluctuations along striations in undoped semi-insulating GaAs Yoshihisa Fujisaki Central Research Laboratory, Hitachi Ltd., 1-280 1-Jigashikoigakubo, Kokuhunji, Tokyo /85, Japan
The microscopic fluctuations of nonstoichiometry were investigated in relation to crystal growth conditions. Striation patterns on undoped semi-insulating GaAs wafers are found to have strong correlations with nonstoichiometry fluctuations. Taking into account the compositional dependence of implanted Si activity, excess arsenic is shown to be incorporated into solid GaAs according to the impurity segregation theory. Consequently, striation patterns were made uniform by suppressing the unstable convection flow in the melt during crystal growth. As a result, the microscopic inhomogeneity of FET (field effect transistor) threshold voltages was greatly reduced to less than two thirds of the conventional value.
1. Introduction Deep centers in semi-insulating GaAs substrates are closely related to the nonstoichiometry, i.e., the deviation from the ideal composition (0.5) of Ga and As atoms in the bulk material. The first attempt to measure the composition of GaAs was carried out by Straumanis and Kim in 1965 [1]. They measured the densities of several specimens grown under different conditions and found that the ratio of Ga/As is not always 1, but usually deviates from unity. This means that the solid phase in the GaAs phase diagram has a finite width. After their work, several researchers followed them but no consistent result had been achieved for a couple of decades [2—4]. Using precise lattice parameter measurements, the author and his co-workers previously found that there is a macroscopic distribution of the composition in bulk materials [5].The size of this inhomogeneity is on the order of 1—10 cm. This inhomogeneity was the origin of long-term contradictory discussions since Straumanis and Kim’s paper. The author also reported that excess As atoms are incorporated into the crystal just as impurities are during the LEC (liquid encapsulated Czochralski) growth. 0022-tJ248/93/$06.OO © 1993
—
In this paper, smaller scale inhomogeneities were studied and a compositional change was found along the striation. Section 2 briefly introduces the specimens and the experimental methods. In section 3, anomalous distributions of lattice parameters in an ingot are shown in relation to the growth condition. In section 4, the activity of implanted Si atoms is used for investigating nonstoichiometry fluctuations along striations. The relationship between the striations and the growth condition is also discussed in that section. In section 5, all the data are gathered to make a model explaining microscopic inhomogeneity of nonstoichiometry.
2. Evaluation methods and specimens The following methods were employed to estimate the distribution of non-stoichiometry in a specimen: (1) Precise lattice parameter measurement. (2) Threshold voltage measurement of test FETs (field effect transistors) fabricated on a specimen wafer. As for the lattice parameter measurement, a unique technique was used, which was developed
Elsevier Science Publishers B.V. All rights reserved
78
Y Fujisaki
/ Nonstoichiometry fluctuations along striations in
undoped SI GaAs
by Takano and Maki [6]. This technique is based on an X-ray double crystal method but the limit for the lattice strain measurement is improved by one order of magnitude from the conventional technique. The details were already reported previously [5] and the author will not mention to it
in the wafer could almost be eliminated. In this paper, this calculation was employed to extract microscopic and small ~h inhomogeneity from the raw data, with good reliability. Observations of striation patterns were accomplished through chemical etching with 10H2S04/
further in the rest of this paper. A major part of the uniformity evaluation of wafer characteristics was performed by measuring the threshold voltages of FETs aligned with a 250 ~m interval. The test FETs were of the nonselfaligned depletion types and were fabricated as follows. The channel layer was made by Si ion implantation, and the subsequent furnace annealing was done with a Si02 encapsulant. Gates and Ohmic contacts were made of Ti/Pt/Au and AuGe/Ni/Au, respectively. The gate length was 2 /Lm and the width was 10 ~m. After measuring the threshold voltages (~h) of an FET array, Fourier analysis was performed on the array data to extract the microscopic fluctuations from the raw data. First, an array of ~ih data was treated as waveform data and Fourier transformed with respect to the measurement position X. Then the filtering was done, i.e., a high-pass filter was multiplied by the l”~~ data array in the Fourier space. The result was reverse Fourier transformed to the real space, giving the microscopic fluctuations of the V~hdata. With this calculation, k~ inhomogeneities such as those originating from the FET fabrication process or dislocations
H,02/H20 solution for 20 to 30 mm at 10°C under light illumination. Since it was very difficult to get high contrast images on (100) surfaces, etching conditions should be carefully modified for each specimen, depending on the impurity and deep level concentrations. The specimen wafers used in this study were all sliced from undoped semi-insulating GaAs grown by the LEC method. They were grown from arsenic-rich melt to achieve a semi-insulating property. All ingots were annealed to eliminate the effect from dislocations and macroscopic nonstoichiometry on the Vlh data. Annealings were performed at 950°C for 24 h in a 20 atm argon atmosphere.
i:j -.~...
5 1.0 x10
Along the Growth Direction
T
_j
=
(d’
d)/d,
(I)
x105 _______________ 1.0
“~ .~
~
~
0.0-
Li
.4-’ -4-’ (0
~d/d
.-.---
/
1.0
In figs. la and ib, typical patterns representing the distribution of lattice strains are shown. The vertical axis representing the lattice strain ~td/d is defined as follows:
tJ
_______________ ____________
•~ 0.0
Ci
3. Inhomogeneity in lattice strain
.4~)
Seed
‘—------~
~
2oa 0
Tail
~
I
I
10
20
30
I
I
40
50
(0
~_J
60
Distance from the Shoulder [mm]
~2.O°’
-30 —20 —10
I
0
I,
10
I
20
30
Distance from the Center [mm]
Fig. 1. Distribution of lattice strain: (a) along the growth direction and (b) perpendicular to the growth direction. The measurements in (a) were carried out along the center of the symmetry of the ingot. The measurement position Y/L = 5/120 mm in (b), where L is the length of the ingot.
Y Fujisaki
~
50
/ Nonstoichiometry fluctuations
=~i~’~
~
the cylindrical symmetry the ingot. posimeasuring points in fig. laofwere on the The center of tions of the plots in fig. lb were 5 mm from the top of the ingot, where the ingot was 120 mm
-~
o
o
C-
Li
-25 —50 _________________________ I I I 0 10 20 30 40 50 60
Distance from the Shoulder [mm] Fig. 2. Microscopic threshold voltage distribution of test FETs along the growth direction. A gradual decrease in the fluctuation can be seen,
where d’ is the lattice parameter of a specimen and d denotes that of a standard HB (horizontal Bridgman) GaAs crystal. Figs. Ia and lb corre-
50
Ix=O.15
long. As reported in previous papers [2,5], the greater lattice strain corresponds to the moe arsenic-rich crystal. It is obvious from these data that the distributions of crystal composition have some inhomogeneous patterns along these two perpendicular directions. The set of data along the growth direction show a gradual increase in arsenic composition from the seed toward the tail end of the ingot overlapped with a frequent fluctuation of nonstoichiometry. Since the initial melt composition is always set slightly arsenic-rich to achieve a semi-
50 I
____________
I~O.35
IcVth=8.66 mvI
~ -4-i
>
I
____________
25
>.
25
e Vth
=
8.36
mvl
0~
0 CLi
-1
50 a, I -30 -20 —10
0
10
20
50
I x = 0.56
25’
I
0
10
20
30
Distance from Center [mm] 50
I
I~Vth=6.99mVl
-25 —50 b, I I -30 —20 -10
30
Distance from Center [mm]
.-~
79
spond to the measurements parallel and perpendicular to the growth direction, respectively. The
~~4~) 25
~
along striations in undoped SI GaAs
I x = 0.74 I I eVth=5.34mVI
‘.—‘ .-~
25~
.0 4~)
>. 0 C-
~Li
0~
0’ —25 —50
Li 0 C-
‘
.~
I
I
I
I
-30 —20 -10
0
10
20
-25 —50
30
Distance from Center [mm]
d I
I
-30 —20 -10
I,
I
I
0
10
20
30
Distance from Center [mm]
Fig. 3. Microscopic threshold voltage distribution of test FETs perpendicular to the growth direction. The fraction solidified corri~spondingto the wafers in figs. 3a, 3b, 3c, and 3d was x 0.15, 0.35, 0.56, and 0.74, respectively. The standard deviations of these four data arrays were 8.66, 8.36, 6.99, and 5.34 mV, respectively.
80
Y Fujisaki
/ Nonstoichiometry fluctuations
along striations in undoped SI GaAs
insulating property [7], the melt becomes more and more arsenic-rich as the crystal grows. Thus, the gradual increase in lattice strain is the result of the condensation of arsenic atoms in the melt [51.The sawtooth-like fluctuation implies growth instabilities along the growth direction because excess arsenic incorporation occurs according to the impurity segregation rule. In the next section, this characteristic pattern is proved to be the reflection from striations running perpendicular to the growth direction.
are processed from raw data to extract microscopic fluctuations. Hereafter, this processed data is called “micro-V~h”.The starting point of the micro-V~hmeasurement was the shoulder part of an ingot. As the measuring point goes from the seed side to the tail end, the microscopic fiuctuation seems to decrease gradually. The same trend can also he seen when the data are taken perpendicular to the growth direction. This trend is shown in fig.3. Four wafers were sliced from one ingot and a micro-J~11analy-
Contrary to fig. la, the distribution of nonstoichiometry is rather simple and smooth perpendicular to the growth direction. These may be the characteristics of LEC method in which both the grown crystal and the melt feel the cylindrically symmetric field.
sis was performed perpendicular to the growth direction. The fraction solidified corresponding to the wafers in figs~.3a, 3b, ~ and 3d were x 0.15, 0.35, 0.56, and 0.74, respectively. Standard deviations of these four V~h arrays were 8.66, 8.36, 6.99, and 5.34 mV, respectively. It was found again that the microscopic ‘~Ii fluctuations were larger in the seed side of an ingot and they were smaller in the tail side. This trend was the same inalong fig. 2,thewhere the direction. fluctuationAlthough data werea taken growth clear dependence was found between micro-VI 1 =
4. Inhomogeneity in electrical property 1/rh distribution along the A typical trend of growth direction is shown in fig. 2. The J/~values
18 rpm
23 rpm
50
~ :
30 rpm
50
____________
‘I I —30 —20 -10
0
I 10
I:
____________
~~5O 20
30
Distance from center [mm]
‘~
I -30 —20
—10
I 0
I 10
I
20
50
50
—10
0
10
20
Tail
30
[mrn[
I 20
30
50 E
>~o4~y*~
Distance from Center
10
Seed
E
_________
I 1,1,1 —20 -10 0
Distance from center [mmj
Seed
E
—30 -20
____________
~~50 —30
30
Distance from Center [mm]
Seed
50
-30
-20 —10
‘50
0
10
20
so
Distance from Center [mm]
Tail
_________
-30
-20 —10
0
10
20
Distance from Center
30
[mm]
Tail
Fig. 4. Dependence of microscopic threshold voltage distribution on the crucible rotation speed. The fluctuation decreases when the rotation speed increases from 18 to 30 rpm.
Y Fujisaki
/ Nonstoichiomet,y fluctuations
along striations in undoped SI GaAs
12
40
5, .
6’
E3°
9O~t,24h
I
~ 20
Annea]
Tail
______
,,,,I
~
5
C
j
I,,I,,,,I,,,,i,,,,
10 15 20 25 30 35
summarized the observed instriation figure patterns The horizontal was measured and and cal represent the6. average striation pitchvertiJ4’~, andaxes its standard deviation (o-J4’~), respectively. As the crucible rotated faster, the average striation pitch Ws became smaller and the striation pattern became more uniform. This may be due to the suppression of unstable convection flow in
I
20
I
30
40
50
Striation Pitch Ws [~u m]
Fig. 5. Dependence of microscopic threshold voltage fluctuation on the crucible rotation speed. The vertical axis corresponds to the standard deviation of the microscopic threshold voltages.
growth was varied and the effect of this perturbation was investigated using micro-k5 analysis. The dependence of micro-V5 fluctuations on the crucible rotation speed is shown in fig. 4. As the crucible rotation speed was increased, the uniformity of micro-J/~hwas improved. Fig. 5 is the clearer explanation for this dependence. The vertical axis corresponds to the standard deviation of micro-V~h. Using the same ingots, striation patterns were observed by chemical etching. Each sample for the striation observation was taken from the center of the shoulder part in each ingot. The distance between two neighboring darker lines in
iI
0
0
Crucible Rotation Speed [rpm]
fluctuations and the position along the growth direction, no correlation was found between micro-l”~h and the position perpendicular to the growth direction. This differs from the case of lattice strains. It implies that it is difficult to get information about striations just from the micro“th analysis. To give some perturbation on striation patterns, the crucible rotation speed during crystal
95O’C24h Anneal
-
10
‘
0
b
____
~4.
2 0
a
Seed
0
~
81
Fig. 6. Relationship between the striation pitch W~and its standard deviation.
the melt. Thus, the data corresponding to the fastest crucible rotation were plotted at the lower left corner of fig. 6. This uW 5 is a more deriable value to compare with the standard deviation of micro-I/h, because both values are the indications of inhomogeneities in an ingot. The relationship between the standard deviations of both micro-Vh and striation pitch J4’~ is shown in fig. 7. A clear dependence of micro-Fh fluctuation on the striaton irregularities is found in this figure.
12 10
u
Seed .
>
0
~
B 6 a
.
~ 20 ~ .6TailI -
0
10
0
95~ 24h Anneal I
_____ ________
I
20
30
40
~ Ws [pm] Fig. 7. Relationship between the standard deviation of microscopic threshold voltage and that of striation pitch W~.
82
Y Fujisaki
/ Nonstoichiomet,y fluctuations along striation,s in
5. Model f~rthe microscopic inhomogeneities In the previous two sections, ~the effect of the striation patterns on inhomogeneities of nonstoichiometry was investigated. Since it is almost impossible to estimate the nonstoichiometry with the spatial resolution of striation patterns, the data should necessarily become indirect. In the restricted spatial resolution, however, we can see a sign of striation effect on lattice strains and on implanted Si activities, If the crystal composition were measured along the crystal growth direction with enough spatial resolution, a set of relatively As-poor and As-rich regions could be repeatedly observed. The data shown in fig. la represent an attempt to do this. Though the spatial resolution of this measurement is not enough, a distinct distribution with a sawtooth form can be seen which might be the reflection from the striation pattern of the crys-
( B
—
undoped SI GaAs
tal. It also indicates that the crystal ingot changes gradually to a more arsenic-rich composition along the crystal growth direction. The information from the VII1 analysis, summarized in figs. 2, 3, and 7, can he understood by making the following two assumptions: (I) A microscopic fluctuation of crystal composition exists along the striation patterns. (2) The activity of implanted Si atoms is strongly affected by this microscopic distribution of the crystal composition. With these assumptions, the compositional change in an ingot along the growth direction can be drawn, as in fig. 8. In this figure, a fluctuating curve A running from seed to tail is the actual composition in a GaAs crystal. The smooth solid line B intersecting the fluctuating curve is the threshold composition from striation pattern etching. Therefore, a striation pattern corresponds to the compositional fluctuation as shown schematically in a small illustration in fig. 8. The referenced curves C and D lying in the lower corner of the figure indicate the dependence of implanted Si activity on the nonstoichiometry of the crystal [31.The activity of implanted Si atoms is expected to he in the region between the two curves. It is well known that semi-insulating GaAs crystals are slightly arsenic-rich and thought to involve a high density of arsenic interstitials [2,5]. Therefore, assumption (1) is quite reasonable since the excess arsenic atoms are incorporated the crystal in the same manner as impurity atoms during the crystal growth. Since the char-
_________ ..______
into
acteristic curve for Si activity is steeper in the seed side than in the tail side, a small compositional change in the seed side is more likely to he
c AfterT Satoetal
D
Composition
Z~ch
Fig. 8. A model explaining the nonstoichiometry distribution along the striation patterns,
exaggerated in the micro-V~5fluctuation at the seed. This is why the microscopic ~‘~h fluctuation is relatively large in the seed and smaller in the tail end of an ingot, as shown in figs. 2 and 3. Fig. 7 is the more direct indication for the nonstoichiometry distribution along striation patterns. As explained in the previous paragraph, the excess arsenic atoms are expected to he incorporated into the crystal according to the impurity segregation theory. This means that a larger cxtent of arsenic will be incorporated into the solid
Y Fujisaki -
/ Nonstoichiometryfluctuations
I
0.9
•WI~
I tL~1~
1 .0
L ~
F—
.
.
With P
I
-1 .2 I -30 -20 -10
I
I
I
0
10
20
83
can be regarded as arsenic as for the crystal composition in GaAs. Therefore, the activity of
Without p
0.8
along striations in undoped SI GaAs
30
Distance from Center [mm] Fig. 9. Improvement of threshold voltage uniformity with phosphorus co-implantation technique. Phosphorus ions were implanted with an acceleration energy of 150 keV and a 3X10°cm’ dose.
the implanted SiThis will corresponds be raised by co-implantation. to phosphorus a shift of a fluctuating compositional curve A in fig. 8 toward the arsenic-rich direction. According to the data from Sato et al. (curves C and D) in fig. 8, the activity of implanted Si becomes relatively insensitive to the compositional change in arsenic rich crystals. This is because the Si activity is enhanced and also made uniform by P co-implantation. It can be concluded that microscopic compositional fluctuation exists and strongly affects the uniformity of implanted Si activity.
6. Summary if the striation pitch is more irregular and larger. Therefore, the fluctuation of composition becomes smaller when the crucible is rotated faster and the striation pattern is uniform. To confirm the model shown in fig. 8, a stoichiometry control experiment was carried out. Hyuga et al. reported on the enhancement of Si activity by phosphorus co-implantation [8]. With this method, V~5 uniformity data were obtained, as shown in fig. 9. The lower curve in fig. 9 corresponds to the V~h distribution of FETs made by Si ion implantation with phosphorus atoms, The phosphorus ions were implanted with the 2 acceleration energy of in150 and a 10I3 to cm dose. The upper curve fig.keV 9 corresponds the control sample made by the same way as the FETs appear in other figures. It is obvious that the microscopic fluctuation was greatly suppressed in the data corresponding to the P co-implanted specimen. The average Si activity was also found to be enhanced by P co-implantation, since smaller V 5 values correspond to higher Si activities. This phenomenon can be explained as follows, using the model shown in fig. 8. Since phosphorus is the same V group as arsenics in the periodic table, phosphorus compensates the arsenic-poor region in GaAs solid. In other words, phosphorus
The microscopic compositional change along striations was investigated. Using lattice parameter measurement and test FET uniformity measurement, the strong correlation was observed between the microscopic fluctuation of Si activity and the striation pattern of the crystal. By increasing the crucible rotation speed during the crystal growth, both the striation patterns and microscopic distribution of Si activity were found to be made quite uniform. Taking into account the compositional dependence of the implanted Si activity and the compositional change of an ingot from seedstriations to tail, the nonstoichiometry fluctuation along is shown to exist. The nonstoichiometry distribution along the striation pattern is the new proof for the fact that excess arsenic atoms are incorporated into solid GaAs, according to the impurity segregation theory.
Acknowledgments The author wishes to thank Mr. R. Nakazono for helping him in the test FET analysis. The author also wishes to thank Dr. Y. Takano for fruitful discussions.
84
Y Fujisaki
/ Nonstoichiometry’ fluctuations along striations in undoped SI GaAs
References [1] MS. Straumanis and CD. Kim, Aeta Cryst. 19 (1965) 256. [2] Y. Takano, T. Ishiba. N. Matsunaga and N. Hashimoto. Japan. J. AppI. Phys. 24 (1985) L239. [3] T. Sato, K. Terashima, H. Emori, S. Ozawa, M. Nakajima, T. Fukuda and K. Ishida. Japan. J. AppI. Phys. 24 (1985) L448. [4] M. Nakajima. T. Sato, T. Fukuda and K. Ishida, in: Semi-Insulating 111—V Materials, Hakone, 1986, Eds. H. Kukimoto and S. Miyazawa (Ohmsha, Tokyo, 1986) p. 181.
[5] Y. Takano, T. Ishiba and Y. Fujisaki, in: Semi-Insulating 111—V Materials, Hakone, 1986, Eds. H. Kukimoto and S. Miyazawa (Ohmsha, Tokyo. 1986) p. 169. [6] Y. Takano and M. Maki, in: Semiconductor Silicon 1973, Eds. H.R. Huff and R.R. Burgers (Electrochemical Society, Princeton, NJ. 1973) p. 469. [7] CU. Kirkpatrik. R.T. Chen, D.E. Holmes, K.R. Elliott and P.M. Asbeck, in: Extended Abstracts 15th Conf. on Solid State and Materials, Tokyo, 1983, p. 145. [8] F. Hyuga, H. Yamazaki, K. Watanahe and J. Osaka, AppI. Phys. Letters 50(1987)1592.
~
CRYSTAL GROWTH
Nonstoichiometry fluctuations along striations in undoped semi-insulating GaAs Yoshihisa Fujisaki Central Research Laboratory, Hitachi Ltd., 1-280 1-Jigashikoigakubo, Kokuhunji, Tokyo /85, Japan
The microscopic fluctuations of nonstoichiometry were investigated in relation to crystal growth conditions. Striation patterns on undoped semi-insulating GaAs wafers are found to have strong correlations with nonstoichiometry fluctuations. Taking into account the compositional dependence of implanted Si activity, excess arsenic is shown to be incorporated into solid GaAs according to the impurity segregation theory. Consequently, striation patterns were made uniform by suppressing the unstable convection flow in the melt during crystal growth. As a result, the microscopic inhomogeneity of FET (field effect transistor) threshold voltages was greatly reduced to less than two thirds of the conventional value.
1. Introduction Deep centers in semi-insulating GaAs substrates are closely related to the nonstoichiometry, i.e., the deviation from the ideal composition (0.5) of Ga and As atoms in the bulk material. The first attempt to measure the composition of GaAs was carried out by Straumanis and Kim in 1965 [1]. They measured the densities of several specimens grown under different conditions and found that the ratio of Ga/As is not always 1, but usually deviates from unity. This means that the solid phase in the GaAs phase diagram has a finite width. After their work, several researchers followed them but no consistent result had been achieved for a couple of decades [2—4]. Using precise lattice parameter measurements, the author and his co-workers previously found that there is a macroscopic distribution of the composition in bulk materials [5].The size of this inhomogeneity is on the order of 1—10 cm. This inhomogeneity was the origin of long-term contradictory discussions since Straumanis and Kim’s paper. The author also reported that excess As atoms are incorporated into the crystal just as impurities are during the LEC (liquid encapsulated Czochralski) growth. 0022-tJ248/93/$06.OO © 1993
—
In this paper, smaller scale inhomogeneities were studied and a compositional change was found along the striation. Section 2 briefly introduces the specimens and the experimental methods. In section 3, anomalous distributions of lattice parameters in an ingot are shown in relation to the growth condition. In section 4, the activity of implanted Si atoms is used for investigating nonstoichiometry fluctuations along striations. The relationship between the striations and the growth condition is also discussed in that section. In section 5, all the data are gathered to make a model explaining microscopic inhomogeneity of nonstoichiometry.
2. Evaluation methods and specimens The following methods were employed to estimate the distribution of non-stoichiometry in a specimen: (1) Precise lattice parameter measurement. (2) Threshold voltage measurement of test FETs (field effect transistors) fabricated on a specimen wafer. As for the lattice parameter measurement, a unique technique was used, which was developed
Elsevier Science Publishers B.V. All rights reserved
78
Y Fujisaki
/ Nonstoichiometry fluctuations along striations in
undoped SI GaAs
by Takano and Maki [6]. This technique is based on an X-ray double crystal method but the limit for the lattice strain measurement is improved by one order of magnitude from the conventional technique. The details were already reported previously [5] and the author will not mention to it
in the wafer could almost be eliminated. In this paper, this calculation was employed to extract microscopic and small ~h inhomogeneity from the raw data, with good reliability. Observations of striation patterns were accomplished through chemical etching with 10H2S04/
further in the rest of this paper. A major part of the uniformity evaluation of wafer characteristics was performed by measuring the threshold voltages of FETs aligned with a 250 ~m interval. The test FETs were of the nonselfaligned depletion types and were fabricated as follows. The channel layer was made by Si ion implantation, and the subsequent furnace annealing was done with a Si02 encapsulant. Gates and Ohmic contacts were made of Ti/Pt/Au and AuGe/Ni/Au, respectively. The gate length was 2 /Lm and the width was 10 ~m. After measuring the threshold voltages (~h) of an FET array, Fourier analysis was performed on the array data to extract the microscopic fluctuations from the raw data. First, an array of ~ih data was treated as waveform data and Fourier transformed with respect to the measurement position X. Then the filtering was done, i.e., a high-pass filter was multiplied by the l”~~ data array in the Fourier space. The result was reverse Fourier transformed to the real space, giving the microscopic fluctuations of the V~hdata. With this calculation, k~ inhomogeneities such as those originating from the FET fabrication process or dislocations
H,02/H20 solution for 20 to 30 mm at 10°C under light illumination. Since it was very difficult to get high contrast images on (100) surfaces, etching conditions should be carefully modified for each specimen, depending on the impurity and deep level concentrations. The specimen wafers used in this study were all sliced from undoped semi-insulating GaAs grown by the LEC method. They were grown from arsenic-rich melt to achieve a semi-insulating property. All ingots were annealed to eliminate the effect from dislocations and macroscopic nonstoichiometry on the Vlh data. Annealings were performed at 950°C for 24 h in a 20 atm argon atmosphere.
i:j -.~...
5 1.0 x10
Along the Growth Direction
T
_j
=
(d’
d)/d,
(I)
x105 _______________ 1.0
“~ .~
~
~
0.0-
Li
.4-’ -4-’ (0
~d/d
.-.---
/
1.0
In figs. la and ib, typical patterns representing the distribution of lattice strains are shown. The vertical axis representing the lattice strain ~td/d is defined as follows:
tJ
_______________ ____________
•~ 0.0
Ci
3. Inhomogeneity in lattice strain
.4~)
Seed
‘—------~
~
2oa 0
Tail
~
I
I
10
20
30
I
I
40
50
(0
~_J
60
Distance from the Shoulder [mm]
~2.O°’
-30 —20 —10
I
0
I,
10
I
20
30
Distance from the Center [mm]
Fig. 1. Distribution of lattice strain: (a) along the growth direction and (b) perpendicular to the growth direction. The measurements in (a) were carried out along the center of the symmetry of the ingot. The measurement position Y/L = 5/120 mm in (b), where L is the length of the ingot.
Y Fujisaki
~
50
/ Nonstoichiometry fluctuations
=~i~’~
~
the cylindrical symmetry the ingot. posimeasuring points in fig. laofwere on the The center of tions of the plots in fig. lb were 5 mm from the top of the ingot, where the ingot was 120 mm
-~
o
o
C-
Li
-25 —50 _________________________ I I I 0 10 20 30 40 50 60
Distance from the Shoulder [mm] Fig. 2. Microscopic threshold voltage distribution of test FETs along the growth direction. A gradual decrease in the fluctuation can be seen,
where d’ is the lattice parameter of a specimen and d denotes that of a standard HB (horizontal Bridgman) GaAs crystal. Figs. Ia and lb corre-
50
Ix=O.15
long. As reported in previous papers [2,5], the greater lattice strain corresponds to the moe arsenic-rich crystal. It is obvious from these data that the distributions of crystal composition have some inhomogeneous patterns along these two perpendicular directions. The set of data along the growth direction show a gradual increase in arsenic composition from the seed toward the tail end of the ingot overlapped with a frequent fluctuation of nonstoichiometry. Since the initial melt composition is always set slightly arsenic-rich to achieve a semi-
50 I
____________
I~O.35
IcVth=8.66 mvI
~ -4-i
>
I
____________
25
>.
25
e Vth
=
8.36
mvl
0~
0 CLi
-1
50 a, I -30 -20 —10
0
10
20
50
I x = 0.56
25’
I
0
10
20
30
Distance from Center [mm] 50
I
I~Vth=6.99mVl
-25 —50 b, I I -30 —20 -10
30
Distance from Center [mm]
.-~
79
spond to the measurements parallel and perpendicular to the growth direction, respectively. The
~~4~) 25
~
along striations in undoped SI GaAs
I x = 0.74 I I eVth=5.34mVI
‘.—‘ .-~
25~
.0 4~)
>. 0 C-
~Li
0~
0’ —25 —50
Li 0 C-
‘
.~
I
I
I
I
-30 —20 -10
0
10
20
-25 —50
30
Distance from Center [mm]
d I
I
-30 —20 -10
I,
I
I
0
10
20
30
Distance from Center [mm]
Fig. 3. Microscopic threshold voltage distribution of test FETs perpendicular to the growth direction. The fraction solidified corri~spondingto the wafers in figs. 3a, 3b, 3c, and 3d was x 0.15, 0.35, 0.56, and 0.74, respectively. The standard deviations of these four data arrays were 8.66, 8.36, 6.99, and 5.34 mV, respectively.
80
Y Fujisaki
/ Nonstoichiometry fluctuations
along striations in undoped SI GaAs
insulating property [7], the melt becomes more and more arsenic-rich as the crystal grows. Thus, the gradual increase in lattice strain is the result of the condensation of arsenic atoms in the melt [51.The sawtooth-like fluctuation implies growth instabilities along the growth direction because excess arsenic incorporation occurs according to the impurity segregation rule. In the next section, this characteristic pattern is proved to be the reflection from striations running perpendicular to the growth direction.
are processed from raw data to extract microscopic fluctuations. Hereafter, this processed data is called “micro-V~h”.The starting point of the micro-V~hmeasurement was the shoulder part of an ingot. As the measuring point goes from the seed side to the tail end, the microscopic fiuctuation seems to decrease gradually. The same trend can also he seen when the data are taken perpendicular to the growth direction. This trend is shown in fig.3. Four wafers were sliced from one ingot and a micro-J~11analy-
Contrary to fig. la, the distribution of nonstoichiometry is rather simple and smooth perpendicular to the growth direction. These may be the characteristics of LEC method in which both the grown crystal and the melt feel the cylindrically symmetric field.
sis was performed perpendicular to the growth direction. The fraction solidified corresponding to the wafers in figs~.3a, 3b, ~ and 3d were x 0.15, 0.35, 0.56, and 0.74, respectively. Standard deviations of these four V~h arrays were 8.66, 8.36, 6.99, and 5.34 mV, respectively. It was found again that the microscopic ‘~Ii fluctuations were larger in the seed side of an ingot and they were smaller in the tail side. This trend was the same inalong fig. 2,thewhere the direction. fluctuationAlthough data werea taken growth clear dependence was found between micro-VI 1 =
4. Inhomogeneity in electrical property 1/rh distribution along the A typical trend of growth direction is shown in fig. 2. The J/~values
18 rpm
23 rpm
50
~ :
30 rpm
50
____________
‘I I —30 —20 -10
0
I 10
I:
____________
~~5O 20
30
Distance from center [mm]
‘~
I -30 —20
—10
I 0
I 10
I
20
50
50
—10
0
10
20
Tail
30
[mrn[
I 20
30
50 E
>~o4~y*~
Distance from Center
10
Seed
E
_________
I 1,1,1 —20 -10 0
Distance from center [mmj
Seed
E
—30 -20
____________
~~50 —30
30
Distance from Center [mm]
Seed
50
-30
-20 —10
‘50
0
10
20
so
Distance from Center [mm]
Tail
_________
-30
-20 —10
0
10
20
Distance from Center
30
[mm]
Tail
Fig. 4. Dependence of microscopic threshold voltage distribution on the crucible rotation speed. The fluctuation decreases when the rotation speed increases from 18 to 30 rpm.
Y Fujisaki
/ Nonstoichiomet,y fluctuations
along striations in undoped SI GaAs
12
40
5, .
6’
E3°
9O~t,24h
I
~ 20
Annea]
Tail
______
,,,,I
~
5
C
j
I,,I,,,,I,,,,i,,,,
10 15 20 25 30 35
summarized the observed instriation figure patterns The horizontal was measured and and cal represent the6. average striation pitchvertiJ4’~, andaxes its standard deviation (o-J4’~), respectively. As the crucible rotated faster, the average striation pitch Ws became smaller and the striation pattern became more uniform. This may be due to the suppression of unstable convection flow in
I
20
I
30
40
50
Striation Pitch Ws [~u m]
Fig. 5. Dependence of microscopic threshold voltage fluctuation on the crucible rotation speed. The vertical axis corresponds to the standard deviation of the microscopic threshold voltages.
growth was varied and the effect of this perturbation was investigated using micro-k5 analysis. The dependence of micro-V5 fluctuations on the crucible rotation speed is shown in fig. 4. As the crucible rotation speed was increased, the uniformity of micro-J/~hwas improved. Fig. 5 is the clearer explanation for this dependence. The vertical axis corresponds to the standard deviation of micro-V~h. Using the same ingots, striation patterns were observed by chemical etching. Each sample for the striation observation was taken from the center of the shoulder part in each ingot. The distance between two neighboring darker lines in
iI
0
0
Crucible Rotation Speed [rpm]
fluctuations and the position along the growth direction, no correlation was found between micro-l”~h and the position perpendicular to the growth direction. This differs from the case of lattice strains. It implies that it is difficult to get information about striations just from the micro“th analysis. To give some perturbation on striation patterns, the crucible rotation speed during crystal
95O’C24h Anneal
-
10
‘
0
b
____
~4.
2 0
a
Seed
0
~
81
Fig. 6. Relationship between the striation pitch W~and its standard deviation.
the melt. Thus, the data corresponding to the fastest crucible rotation were plotted at the lower left corner of fig. 6. This uW 5 is a more deriable value to compare with the standard deviation of micro-I/h, because both values are the indications of inhomogeneities in an ingot. The relationship between the standard deviations of both micro-Vh and striation pitch J4’~ is shown in fig. 7. A clear dependence of micro-Fh fluctuation on the striaton irregularities is found in this figure.
12 10
u
Seed .
>
0
~
B 6 a
.
~ 20 ~ .6TailI -
0
10
0
95~ 24h Anneal I
_____ ________
I
20
30
40
~ Ws [pm] Fig. 7. Relationship between the standard deviation of microscopic threshold voltage and that of striation pitch W~.
82
Y Fujisaki
/ Nonstoichiomet,y fluctuations along striation,s in
5. Model f~rthe microscopic inhomogeneities In the previous two sections, ~the effect of the striation patterns on inhomogeneities of nonstoichiometry was investigated. Since it is almost impossible to estimate the nonstoichiometry with the spatial resolution of striation patterns, the data should necessarily become indirect. In the restricted spatial resolution, however, we can see a sign of striation effect on lattice strains and on implanted Si activities, If the crystal composition were measured along the crystal growth direction with enough spatial resolution, a set of relatively As-poor and As-rich regions could be repeatedly observed. The data shown in fig. la represent an attempt to do this. Though the spatial resolution of this measurement is not enough, a distinct distribution with a sawtooth form can be seen which might be the reflection from the striation pattern of the crys-
( B
—
undoped SI GaAs
tal. It also indicates that the crystal ingot changes gradually to a more arsenic-rich composition along the crystal growth direction. The information from the VII1 analysis, summarized in figs. 2, 3, and 7, can he understood by making the following two assumptions: (I) A microscopic fluctuation of crystal composition exists along the striation patterns. (2) The activity of implanted Si atoms is strongly affected by this microscopic distribution of the crystal composition. With these assumptions, the compositional change in an ingot along the growth direction can be drawn, as in fig. 8. In this figure, a fluctuating curve A running from seed to tail is the actual composition in a GaAs crystal. The smooth solid line B intersecting the fluctuating curve is the threshold composition from striation pattern etching. Therefore, a striation pattern corresponds to the compositional fluctuation as shown schematically in a small illustration in fig. 8. The referenced curves C and D lying in the lower corner of the figure indicate the dependence of implanted Si activity on the nonstoichiometry of the crystal [31.The activity of implanted Si atoms is expected to he in the region between the two curves. It is well known that semi-insulating GaAs crystals are slightly arsenic-rich and thought to involve a high density of arsenic interstitials [2,5]. Therefore, assumption (1) is quite reasonable since the excess arsenic atoms are incorporated the crystal in the same manner as impurity atoms during the crystal growth. Since the char-
_________ ..______
into
acteristic curve for Si activity is steeper in the seed side than in the tail side, a small compositional change in the seed side is more likely to he
c AfterT Satoetal
D
Composition
Z~ch
Fig. 8. A model explaining the nonstoichiometry distribution along the striation patterns,
exaggerated in the micro-V~5fluctuation at the seed. This is why the microscopic ~‘~h fluctuation is relatively large in the seed and smaller in the tail end of an ingot, as shown in figs. 2 and 3. Fig. 7 is the more direct indication for the nonstoichiometry distribution along striation patterns. As explained in the previous paragraph, the excess arsenic atoms are expected to he incorporated into the crystal according to the impurity segregation theory. This means that a larger cxtent of arsenic will be incorporated into the solid
Y Fujisaki -
/ Nonstoichiometryfluctuations
I
0.9
•WI~
I tL~1~
1 .0
L ~
F—
.
.
With P
I
-1 .2 I -30 -20 -10
I
I
I
0
10
20
83
can be regarded as arsenic as for the crystal composition in GaAs. Therefore, the activity of
Without p
0.8
along striations in undoped SI GaAs
30
Distance from Center [mm] Fig. 9. Improvement of threshold voltage uniformity with phosphorus co-implantation technique. Phosphorus ions were implanted with an acceleration energy of 150 keV and a 3X10°cm’ dose.
the implanted SiThis will corresponds be raised by co-implantation. to phosphorus a shift of a fluctuating compositional curve A in fig. 8 toward the arsenic-rich direction. According to the data from Sato et al. (curves C and D) in fig. 8, the activity of implanted Si becomes relatively insensitive to the compositional change in arsenic rich crystals. This is because the Si activity is enhanced and also made uniform by P co-implantation. It can be concluded that microscopic compositional fluctuation exists and strongly affects the uniformity of implanted Si activity.
6. Summary if the striation pitch is more irregular and larger. Therefore, the fluctuation of composition becomes smaller when the crucible is rotated faster and the striation pattern is uniform. To confirm the model shown in fig. 8, a stoichiometry control experiment was carried out. Hyuga et al. reported on the enhancement of Si activity by phosphorus co-implantation [8]. With this method, V~5 uniformity data were obtained, as shown in fig. 9. The lower curve in fig. 9 corresponds to the V~h distribution of FETs made by Si ion implantation with phosphorus atoms, The phosphorus ions were implanted with the 2 acceleration energy of in150 and a 10I3 to cm dose. The upper curve fig.keV 9 corresponds the control sample made by the same way as the FETs appear in other figures. It is obvious that the microscopic fluctuation was greatly suppressed in the data corresponding to the P co-implanted specimen. The average Si activity was also found to be enhanced by P co-implantation, since smaller V 5 values correspond to higher Si activities. This phenomenon can be explained as follows, using the model shown in fig. 8. Since phosphorus is the same V group as arsenics in the periodic table, phosphorus compensates the arsenic-poor region in GaAs solid. In other words, phosphorus
The microscopic compositional change along striations was investigated. Using lattice parameter measurement and test FET uniformity measurement, the strong correlation was observed between the microscopic fluctuation of Si activity and the striation pattern of the crystal. By increasing the crucible rotation speed during the crystal growth, both the striation patterns and microscopic distribution of Si activity were found to be made quite uniform. Taking into account the compositional dependence of the implanted Si activity and the compositional change of an ingot from seedstriations to tail, the nonstoichiometry fluctuation along is shown to exist. The nonstoichiometry distribution along the striation pattern is the new proof for the fact that excess arsenic atoms are incorporated into solid GaAs, according to the impurity segregation theory.
Acknowledgments The author wishes to thank Mr. R. Nakazono for helping him in the test FET analysis. The author also wishes to thank Dr. Y. Takano for fruitful discussions.
84
Y Fujisaki
/ Nonstoichiometry’ fluctuations along striations in undoped SI GaAs
References [1] MS. Straumanis and CD. Kim, Aeta Cryst. 19 (1965) 256. [2] Y. Takano, T. Ishiba. N. Matsunaga and N. Hashimoto. Japan. J. AppI. Phys. 24 (1985) L239. [3] T. Sato, K. Terashima, H. Emori, S. Ozawa, M. Nakajima, T. Fukuda and K. Ishida. Japan. J. AppI. Phys. 24 (1985) L448. [4] M. Nakajima. T. Sato, T. Fukuda and K. Ishida, in: Semi-Insulating 111—V Materials, Hakone, 1986, Eds. H. Kukimoto and S. Miyazawa (Ohmsha, Tokyo, 1986) p. 181.
[5] Y. Takano, T. Ishiba and Y. Fujisaki, in: Semi-Insulating 111—V Materials, Hakone, 1986, Eds. H. Kukimoto and S. Miyazawa (Ohmsha, Tokyo. 1986) p. 169. [6] Y. Takano and M. Maki, in: Semiconductor Silicon 1973, Eds. H.R. Huff and R.R. Burgers (Electrochemical Society, Princeton, NJ. 1973) p. 469. [7] CU. Kirkpatrik. R.T. Chen, D.E. Holmes, K.R. Elliott and P.M. Asbeck, in: Extended Abstracts 15th Conf. on Solid State and Materials, Tokyo, 1983, p. 145. [8] F. Hyuga, H. Yamazaki, K. Watanahe and J. Osaka, AppI. Phys. Letters 50(1987)1592.