J. Mater. Sci. Technol. ›› 2019, Vol. 35 ›› Issue (5): 946-956.DOI: 10.1016/j.jmst.2018.11.007
• Orginal Article • Previous Articles
Liu Qing, junLi He*(), Zhang Yulei*(), Zhao Zhigang
Received:
2018-07-26
Accepted:
2018-10-23
Online:
2019-05-10
Published:
2019-05-23
Contact:
junLi He,Zhang Yulei
Liu Qing, junLi He, Zhang Yulei, Zhao Zhigang. Pattern recognition of messily grown nanowire morphologies applying multi-layer connected self-organized feature maps[J]. J. Mater. Sci. Technol., 2019, 35(5): 946-956.
Fig. 2. Schematic of inter-layer learning procedure. (a)-(c) Building inter-layer connections between winning neurons and the neighbored ones representing the training samples with mapping relations; (d)-(f) boosting of the repeated connections.
Fig. 3. Model of the virtual training samples. (a) Generated virtual morphology of messily grown nanowires; (b) and (c) structure of the virtual nanowire and each line section.
Fig. 4. SEM images of messily grown Si nanowire morphologies [9]. (a) M1 synthesized at 950?°C for 2?h; (b) M2 synthesized at 1000?°C for 2?h; (c) M3 synthesized at 1100?°C for 0.5?h.
ρA | 2rexp (nm) | rconv | lexp (μm) | φˉ(deg) | Δφ(deg) | |
---|---|---|---|---|---|---|
M1 | 0.133 | 26.89 | 0.098 | 0.56 | 81.83 | 9.27 |
M2 | 0.273 | 27.74 | 0.085 | 1.80 | 93.29 | 7.85 |
M3 | 0.128 | 29.14 | 0.050 | 1.46 | 96.06 | 5.20 |
Table 1 Average converted radii, nanowire length, direction angles and growth direction change of morphology M1-M3 [9].
ρA | 2rexp (nm) | rconv | lexp (μm) | φˉ(deg) | Δφ(deg) | |
---|---|---|---|---|---|---|
M1 | 0.133 | 26.89 | 0.098 | 0.56 | 81.83 | 9.27 |
M2 | 0.273 | 27.74 | 0.085 | 1.80 | 93.29 | 7.85 |
M3 | 0.128 | 29.14 | 0.050 | 1.46 | 96.06 | 5.20 |
Candidate set with nanowire quantity and length (Nsim, lsim) | |||||||
---|---|---|---|---|---|---|---|
M1 | UL | rsim?=?0.1 | (50,5) | (70,3.5) | (100,2.5) | - | (200,1) |
UR | (50,10) | (70,8) | (100,5) | (150,3) | (200,2.5) | ||
LL | (50,6) | (70,4.5) | (100,3) | (150,2) | (200,1.5) | ||
LR | (50,5) | (70,3.5) | (100,2.5) | - | (200,1) | ||
M2 | UL | rsim?=?0.05 | - | (70,17) | (100,10) | (150,6) | (200,4.5) |
rsim?=?0.1 | - | (70,12) | (100,8) | (150,4.5) | (200,3.5) | ||
UR | rsim?=?0.05 | - | (70,25) | (100,13) | (150,8) | (200,6) | |
rsim?=?0.1 | - | (70,17) | (100,10) | (150,6) | (200,4) | ||
LL | rsim?=?0.05 | - | (70,17) | (100,10) | (150,6) | (200,4.5) | |
rsim?=?0.1 | - | (70,12) | (100,8) | (150,4.5) | (200,3.5) | ||
LR | rsim?=?0.05 | - | (70,15) | (100,9) | (150,6) | (200,4) | |
rsim?=?0.1 | - | (70,10) | (100,7) | (150,4) | (200,3) | ||
M3 | UL | rsim?=?0.05 | (50,10) | (70,6.5) | (100,4) | (150,2.5) | (200,2) |
UR | (50,10) | (70,7) | (100,4) | (150,3) | (200,2) | ||
LL | (50,8) | (70,5.5) | (100,4) | (150,2.5) | - | ||
LR | (50,7) | (70,5) | (100,3) | (150,2) | - |
Table 2 Candidate sets including virtual training samples with similar area ratios to the experimental morphologies.
Candidate set with nanowire quantity and length (Nsim, lsim) | |||||||
---|---|---|---|---|---|---|---|
M1 | UL | rsim?=?0.1 | (50,5) | (70,3.5) | (100,2.5) | - | (200,1) |
UR | (50,10) | (70,8) | (100,5) | (150,3) | (200,2.5) | ||
LL | (50,6) | (70,4.5) | (100,3) | (150,2) | (200,1.5) | ||
LR | (50,5) | (70,3.5) | (100,2.5) | - | (200,1) | ||
M2 | UL | rsim?=?0.05 | - | (70,17) | (100,10) | (150,6) | (200,4.5) |
rsim?=?0.1 | - | (70,12) | (100,8) | (150,4.5) | (200,3.5) | ||
UR | rsim?=?0.05 | - | (70,25) | (100,13) | (150,8) | (200,6) | |
rsim?=?0.1 | - | (70,17) | (100,10) | (150,6) | (200,4) | ||
LL | rsim?=?0.05 | - | (70,17) | (100,10) | (150,6) | (200,4.5) | |
rsim?=?0.1 | - | (70,12) | (100,8) | (150,4.5) | (200,3.5) | ||
LR | rsim?=?0.05 | - | (70,15) | (100,9) | (150,6) | (200,4) | |
rsim?=?0.1 | - | (70,10) | (100,7) | (150,4) | (200,3) | ||
M3 | UL | rsim?=?0.05 | (50,10) | (70,6.5) | (100,4) | (150,2.5) | (200,2) |
UR | (50,10) | (70,7) | (100,4) | (150,3) | (200,2) | ||
LL | (50,8) | (70,5.5) | (100,4) | (150,2.5) | - | ||
LR | (50,7) | (70,5) | (100,3) | (150,2) | - |
Fig. 7. Nanowire length predictions and neuron responses of the as-trained double-layer SOFMs. (a) Nanowire length of the experimental morphologies predicted by the as-trained SOFMs; (b) neural responses to input H(λx) of M3-UL.
N?exp | l?exp (μm) | |
---|---|---|
MM1 | 488 | 0.55 |
MM2 | 478 | 1.58 |
MM3 | 465 | 1.53 |
Table 3 Estimated nanowire quantity and length of experimental morphologies.
N?exp | l?exp (μm) | |
---|---|---|
MM1 | 488 | 0.55 |
MM2 | 478 | 1.58 |
MM3 | 465 | 1.53 |
Fig. 9. Evolution of winning neuron locations with training times on layer N1. The inputs are candidate virtual training samples for M3-UL. Winning neuron locations with (a) 1 training time, (b) 10 training times, (c) 100 training times, (d) 200 training times, (e) 300 training times. (f) Final location and inter-layer connection of winning neurons.
Fig. 10. Evolution of Euclidean distance between winning neurons and the corresponding training samples. The inputs are candidate virtual training samples for M3-UL. (a) Distance evolution of X1, X3 and X5 on layer N1; (b) distance evolution of Y1, Y3 and Y5 on layer N2.
Fig. 11. Inter-layer weights of winning neurons representing Hλx and Nsim,lsim of one virtual training sample {X3(λx),Y3}. Numbers on the edges of N1 and N2 represent the sequence number of the neurons. (a) Inter-layer connections of winning neuron No.17 on layer N1; (b) inter-layer connections of winning neuron No.55 on layer N2.
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