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Scalable Silicon Nanostructuring for Thermoelectric Applications
The current limitations of commercially available thermoelectric (TE) generators include their incompatibility with human body applications due to the toxicity of commonly used alloys and possible future shortage of raw materials (Bi-Sb-Te and Se). In this respect, exploiting silicon as an environme...
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Published in: | Journal of electronic materials 2013-07, Vol.42 (7), p.2114-2118 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | The current limitations of commercially available thermoelectric (TE) generators include their incompatibility with human body applications due to the toxicity of commonly used alloys and possible future shortage of raw materials (Bi-Sb-Te and Se). In this respect, exploiting silicon as an environmentally friendly candidate for thermoelectric applications is a promising alternative since it is an abundant, ecofriendly semiconductor for which there already exists an infrastructure for low-cost and high-yield processing. Contrary to the existing approaches, where
n
/
p
-legs were either heavily doped to an optimal carrier concentration of 10
19
cm
−3
or morphologically modified by increasing their roughness, in this work improved thermoelectric performance was achieved in smooth silicon nanostructures with low doping concentration (1.5 × 10
15
cm
−3
). Scalable, highly reproducible e-beam lithographies, which are compatible with nanoimprint and followed by deep reactive-ion etching (DRIE), were employed to produce arrays of regularly spaced nanopillars of 400 nm height with diameters varying from 140 nm to 300 nm. A potential Seebeck microprobe (PSM) was used to measure the Seebeck coefficients of such nanostructures. This resulted in values ranging from −75
μ
V/K to −120
μ
V/K for
n
-type and 100
μ
V/K to 140
μ
V/K for
p
-type, which are significant improvements over previously reported data. |
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ISSN: | 0361-5235 1543-186X |
DOI: | 10.1007/s11664-013-2539-6 |