Abstract

While quantum dots-in-a-well (DWELL) infrared photodetectors have the feature that their spectral responses can be shifted continuously by varying the applied bias, the width of the spectral response at any applied bias is not sufficiently narrow for use in multispectral sensing without the aid of spectral filters. To achieve higher spectral resolutions without using physical spectral filters, algorithms have been developed for post-processing the DWELL’s bias-dependent photocurrents resulting from probing an object of interest repeatedly over a wide range of applied biases. At the heart of these algorithms is the ability to approximate an arbitrary spectral filter, which we desire the DWELL-algorithm combination to mimic, by forming a weighted superposition of the DWELL’s non-orthogonal spectral responses over a range of applied biases. However, these algorithms assume availability of abundant DWELL data over a large number of applied biases (>30), leading to large overall acquisition times in proportion with the number of biases. This paper reports a new multispectral sensing algorithm to substantially compress the number of necessary bias values subject to a prescribed performance level across multiple sensing applications. The algorithm identifies a minimal set of biases to be used in sensing only the relevant spectral information for remote-sensing applications of interest. Experimental results on target spectrometry and classification demonstrate a reduction in the number of required biases by a factor of 7 (e.g., from 30 to 4). The tradeoff between performance and bias compression is thoroughly investigated.

© 2011 OSA

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    [CrossRef]

2011

P. Vines, C. H. Tan, J. P. R. David, R. S. Attaluri, T. E. Vandervelde, S. Krishna, W.-Y. Jang, and M. M. Hayat, “Versatile spectral imaging with an algorithm-based spectrometer using highly tuneable quantum dot infrared photodetectors,” IEEE J. Quantum Electron. 47(2), 190–197 (2011).
[CrossRef]

B. S. Paskaleva, W.-Y. Jang, S. C. Bender, Y. D. Sharma, S. Krishna, and M. M. Hayat, “Multispectral classification with bias-tunable quantum dots-in-a-well focal plane arrays,” IEEE Sens. J. 11(6), 1342–1351 (2011).
[CrossRef]

2009

W.-Y. Jang, M. M. Hayat, J. S. Tyo, R. S. Attaluri, T. E. Vandervelde, Y. D. Sharma, R. Shenoi, A. Stintz, E. R. Cantwell, S. C. Bender, S. J. Lee, S. K. Noh, and S. Krishna, “Demonstration of bias controlled algorithmic tuning of quantum dots in a well (DWELL) MidIR detectors,” IEEE J. Quantum Electron. 45(6), 674–683 (2009).
[CrossRef]

2008

B. Paskaleva, M. M. Hayat, Z. Wang, J. S. Tyo, and S. Krishna, “Canonical correlation feature selection for sensors with overlapping bands: theory and application,” IEEE Trans. Geosci. Rem. Sens. 46(10), 3346–3358 (2008).
[CrossRef]

2007

J. C. Campbell and A. Madhukar, “Quantum-dot infrared photodetectors,” Proc. IEEE 95(9), 1815–1827 (2007).
[CrossRef]

2006

2005

S. Krishna, “Quantum dots-in-a-well infrared photodetectors,” J. Phys. D Appl. Phys. 38(13), 2142–2150 (2005).
[CrossRef]

C. A. Musca, J. Antoszewski, K. J. Winchester, A. J. Keating, T. Nguyen, K. K. M. B. D. Silva, J. M. Dell, L. Faraone, P. Mitra, J. D. Beck, M. R. Skokan, and J. E. Robinson, “Monolithic integration of an infrared photon detector with a MEMS-based tunable filter,” IEEE Electron Dev. Lett. 26(12), 888–890 (2005).
[CrossRef]

S. F. Cotter, B. D. Rao, Kjersti Engan, and K. Kreutz-Delgado, “Sparse Solutions to Linear Inverse Problems With Multiple Measurement Vectors,” IEEE Trans. Signal Process. 53(7), 2477–2488 (2005).
[CrossRef]

2004

2003

D. Tezcan, S. Eminoglu, and T. Akin, “A Low-Cost Uncooled Infrared Microbolometer Detector in Standard CMOS Technology,” IEEE Trans. Electron. Dev. 50(2), 494–502 (2003).
[CrossRef]

S. Krishna, S. Raghavan, G. von Winckel, A. Stintz, G. Ariyawansa, S. G. Matsik, and A. G. U. Perera, “Three-color (λp1~3.8μm, λp2~8.5μm, λp3~23.2μm) InAs/InGaAs quantum-dots-in-a-well detector,” Appl. Phys. Lett. 83(14), 2745–2747 (2003).
[CrossRef]

2002

N. Gupta, R. Dahmani, and S. Choy, “Acousto-optic tunable filter based visible- to near-infrared spectropolarimetric imager,” Opt. Eng. 41(5), 1033–1038 (2002).
[CrossRef]

A. Majumdar, K. K. Choi, J. L. Reno, L. P. Rokhinson, and D. C. Tsui, “Two-color quantum-well infrared photodetector with voltage tunable peaks,” Appl. Phys. Lett. 80(5), 707–709 (2002).
[CrossRef]

1997

G. Davis, S. Mallat, and M. Avellandeda, “Adaptive greedy approximations,” Constr. Approx. 13(1), 57–98 (1997).

K. W. Berryman, S. A. Lyon, and M. Segev, “Mid-infrared photoconductivity in InAs quantum dots,” Appl. Phys. Lett. 70(14), 1861 (1997).
[CrossRef]

1993

B. F. Levine, “Quantum-well infrared photodetectors,” J. Appl. Phys. 74(8), R1–R81 (1993).
[CrossRef]

1991

1984

D. A. B. Miller, D. S. Chemla, T. C. Damen, A. C. Gossard, W. Wiegmann, T. H. Wood, and C. A. Burrus, “Band-edge electroabsorption in quantum well structures: The quantum-confined stark effect,” Phys. Rev. Lett. 53(22), 2173–2176 (1984).
[CrossRef]

Akin, T.

D. Tezcan, S. Eminoglu, and T. Akin, “A Low-Cost Uncooled Infrared Microbolometer Detector in Standard CMOS Technology,” IEEE Trans. Electron. Dev. 50(2), 494–502 (2003).
[CrossRef]

Annamalai, S.

Antoszewski, J.

C. A. Musca, J. Antoszewski, K. J. Winchester, A. J. Keating, T. Nguyen, K. K. M. B. D. Silva, J. M. Dell, L. Faraone, P. Mitra, J. D. Beck, M. R. Skokan, and J. E. Robinson, “Monolithic integration of an infrared photon detector with a MEMS-based tunable filter,” IEEE Electron Dev. Lett. 26(12), 888–890 (2005).
[CrossRef]

Ariyawansa, G.

S. Krishna, S. Raghavan, G. von Winckel, A. Stintz, G. Ariyawansa, S. G. Matsik, and A. G. U. Perera, “Three-color (λp1~3.8μm, λp2~8.5μm, λp3~23.2μm) InAs/InGaAs quantum-dots-in-a-well detector,” Appl. Phys. Lett. 83(14), 2745–2747 (2003).
[CrossRef]

Attaluri, R. S.

P. Vines, C. H. Tan, J. P. R. David, R. S. Attaluri, T. E. Vandervelde, S. Krishna, W.-Y. Jang, and M. M. Hayat, “Versatile spectral imaging with an algorithm-based spectrometer using highly tuneable quantum dot infrared photodetectors,” IEEE J. Quantum Electron. 47(2), 190–197 (2011).
[CrossRef]

W.-Y. Jang, M. M. Hayat, J. S. Tyo, R. S. Attaluri, T. E. Vandervelde, Y. D. Sharma, R. Shenoi, A. Stintz, E. R. Cantwell, S. C. Bender, S. J. Lee, S. K. Noh, and S. Krishna, “Demonstration of bias controlled algorithmic tuning of quantum dots in a well (DWELL) MidIR detectors,” IEEE J. Quantum Electron. 45(6), 674–683 (2009).
[CrossRef]

Avellandeda, M.

G. Davis, S. Mallat, and M. Avellandeda, “Adaptive greedy approximations,” Constr. Approx. 13(1), 57–98 (1997).

Beck, J. D.

C. A. Musca, J. Antoszewski, K. J. Winchester, A. J. Keating, T. Nguyen, K. K. M. B. D. Silva, J. M. Dell, L. Faraone, P. Mitra, J. D. Beck, M. R. Skokan, and J. E. Robinson, “Monolithic integration of an infrared photon detector with a MEMS-based tunable filter,” IEEE Electron Dev. Lett. 26(12), 888–890 (2005).
[CrossRef]

Bender, S. C.

B. S. Paskaleva, W.-Y. Jang, S. C. Bender, Y. D. Sharma, S. Krishna, and M. M. Hayat, “Multispectral classification with bias-tunable quantum dots-in-a-well focal plane arrays,” IEEE Sens. J. 11(6), 1342–1351 (2011).
[CrossRef]

W.-Y. Jang, M. M. Hayat, J. S. Tyo, R. S. Attaluri, T. E. Vandervelde, Y. D. Sharma, R. Shenoi, A. Stintz, E. R. Cantwell, S. C. Bender, S. J. Lee, S. K. Noh, and S. Krishna, “Demonstration of bias controlled algorithmic tuning of quantum dots in a well (DWELL) MidIR detectors,” IEEE J. Quantum Electron. 45(6), 674–683 (2009).
[CrossRef]

Berryman, K. W.

K. W. Berryman, S. A. Lyon, and M. Segev, “Mid-infrared photoconductivity in InAs quantum dots,” Appl. Phys. Lett. 70(14), 1861 (1997).
[CrossRef]

Burrus, C. A.

D. A. B. Miller, D. S. Chemla, T. C. Damen, A. C. Gossard, W. Wiegmann, T. H. Wood, and C. A. Burrus, “Band-edge electroabsorption in quantum well structures: The quantum-confined stark effect,” Phys. Rev. Lett. 53(22), 2173–2176 (1984).
[CrossRef]

Campbell, J. C.

J. C. Campbell and A. Madhukar, “Quantum-dot infrared photodetectors,” Proc. IEEE 95(9), 1815–1827 (2007).
[CrossRef]

Cantwell, E. R.

W.-Y. Jang, M. M. Hayat, J. S. Tyo, R. S. Attaluri, T. E. Vandervelde, Y. D. Sharma, R. Shenoi, A. Stintz, E. R. Cantwell, S. C. Bender, S. J. Lee, S. K. Noh, and S. Krishna, “Demonstration of bias controlled algorithmic tuning of quantum dots in a well (DWELL) MidIR detectors,” IEEE J. Quantum Electron. 45(6), 674–683 (2009).
[CrossRef]

Chemla, D. S.

D. A. B. Miller, D. S. Chemla, T. C. Damen, A. C. Gossard, W. Wiegmann, T. H. Wood, and C. A. Burrus, “Band-edge electroabsorption in quantum well structures: The quantum-confined stark effect,” Phys. Rev. Lett. 53(22), 2173–2176 (1984).
[CrossRef]

Choi, K. K.

A. Majumdar, K. K. Choi, J. L. Reno, L. P. Rokhinson, and D. C. Tsui, “Two-color quantum-well infrared photodetector with voltage tunable peaks,” Appl. Phys. Lett. 80(5), 707–709 (2002).
[CrossRef]

Choy, S.

N. Gupta, R. Dahmani, and S. Choy, “Acousto-optic tunable filter based visible- to near-infrared spectropolarimetric imager,” Opt. Eng. 41(5), 1033–1038 (2002).
[CrossRef]

Cotter, S. F.

S. F. Cotter, B. D. Rao, Kjersti Engan, and K. Kreutz-Delgado, “Sparse Solutions to Linear Inverse Problems With Multiple Measurement Vectors,” IEEE Trans. Signal Process. 53(7), 2477–2488 (2005).
[CrossRef]

Dahmani, R.

N. Gupta, R. Dahmani, and S. Choy, “Acousto-optic tunable filter based visible- to near-infrared spectropolarimetric imager,” Opt. Eng. 41(5), 1033–1038 (2002).
[CrossRef]

Damen, T. C.

D. A. B. Miller, D. S. Chemla, T. C. Damen, A. C. Gossard, W. Wiegmann, T. H. Wood, and C. A. Burrus, “Band-edge electroabsorption in quantum well structures: The quantum-confined stark effect,” Phys. Rev. Lett. 53(22), 2173–2176 (1984).
[CrossRef]

David, J. P. R.

P. Vines, C. H. Tan, J. P. R. David, R. S. Attaluri, T. E. Vandervelde, S. Krishna, W.-Y. Jang, and M. M. Hayat, “Versatile spectral imaging with an algorithm-based spectrometer using highly tuneable quantum dot infrared photodetectors,” IEEE J. Quantum Electron. 47(2), 190–197 (2011).
[CrossRef]

Davis, G.

G. Davis, S. Mallat, and M. Avellandeda, “Adaptive greedy approximations,” Constr. Approx. 13(1), 57–98 (1997).

Dell, J. M.

C. A. Musca, J. Antoszewski, K. J. Winchester, A. J. Keating, T. Nguyen, K. K. M. B. D. Silva, J. M. Dell, L. Faraone, P. Mitra, J. D. Beck, M. R. Skokan, and J. E. Robinson, “Monolithic integration of an infrared photon detector with a MEMS-based tunable filter,” IEEE Electron Dev. Lett. 26(12), 888–890 (2005).
[CrossRef]

Dowd, P.

Eminoglu, S.

D. Tezcan, S. Eminoglu, and T. Akin, “A Low-Cost Uncooled Infrared Microbolometer Detector in Standard CMOS Technology,” IEEE Trans. Electron. Dev. 50(2), 494–502 (2003).
[CrossRef]

Faraone, L.

C. A. Musca, J. Antoszewski, K. J. Winchester, A. J. Keating, T. Nguyen, K. K. M. B. D. Silva, J. M. Dell, L. Faraone, P. Mitra, J. D. Beck, M. R. Skokan, and J. E. Robinson, “Monolithic integration of an infrared photon detector with a MEMS-based tunable filter,” IEEE Electron Dev. Lett. 26(12), 888–890 (2005).
[CrossRef]

Gossard, A. C.

D. A. B. Miller, D. S. Chemla, T. C. Damen, A. C. Gossard, W. Wiegmann, T. H. Wood, and C. A. Burrus, “Band-edge electroabsorption in quantum well structures: The quantum-confined stark effect,” Phys. Rev. Lett. 53(22), 2173–2176 (1984).
[CrossRef]

Gupta, N.

N. Gupta, R. Dahmani, and S. Choy, “Acousto-optic tunable filter based visible- to near-infrared spectropolarimetric imager,” Opt. Eng. 41(5), 1033–1038 (2002).
[CrossRef]

Hayat, M. M.

B. S. Paskaleva, W.-Y. Jang, S. C. Bender, Y. D. Sharma, S. Krishna, and M. M. Hayat, “Multispectral classification with bias-tunable quantum dots-in-a-well focal plane arrays,” IEEE Sens. J. 11(6), 1342–1351 (2011).
[CrossRef]

P. Vines, C. H. Tan, J. P. R. David, R. S. Attaluri, T. E. Vandervelde, S. Krishna, W.-Y. Jang, and M. M. Hayat, “Versatile spectral imaging with an algorithm-based spectrometer using highly tuneable quantum dot infrared photodetectors,” IEEE J. Quantum Electron. 47(2), 190–197 (2011).
[CrossRef]

W.-Y. Jang, M. M. Hayat, J. S. Tyo, R. S. Attaluri, T. E. Vandervelde, Y. D. Sharma, R. Shenoi, A. Stintz, E. R. Cantwell, S. C. Bender, S. J. Lee, S. K. Noh, and S. Krishna, “Demonstration of bias controlled algorithmic tuning of quantum dots in a well (DWELL) MidIR detectors,” IEEE J. Quantum Electron. 45(6), 674–683 (2009).
[CrossRef]

B. Paskaleva, M. M. Hayat, Z. Wang, J. S. Tyo, and S. Krishna, “Canonical correlation feature selection for sensors with overlapping bands: theory and application,” IEEE Trans. Geosci. Rem. Sens. 46(10), 3346–3358 (2008).
[CrossRef]

Ü. Sakoğlu, M. M. Hayat, J. S. Tyo, P. Dowd, S. Annamalai, K. T. Posani, and S. Krishna, “Statistical adaptive sensing by detectors with spectrally overlapping bands,” Appl. Opt. 45(28), 7224–7234 (2006).
[CrossRef] [PubMed]

Ü. Sakoğlu, J. S. Tyo, M. M. Hayat, S. Raghavan, and S. Krishna, “Spectrally adaptive infrared photodetectors with bias-tunable quantum dots,” J. Opt. Soc. Am. B 21(1), 7–17 (2004).
[CrossRef]

Jahns, J.

Jang, W.-Y.

B. S. Paskaleva, W.-Y. Jang, S. C. Bender, Y. D. Sharma, S. Krishna, and M. M. Hayat, “Multispectral classification with bias-tunable quantum dots-in-a-well focal plane arrays,” IEEE Sens. J. 11(6), 1342–1351 (2011).
[CrossRef]

P. Vines, C. H. Tan, J. P. R. David, R. S. Attaluri, T. E. Vandervelde, S. Krishna, W.-Y. Jang, and M. M. Hayat, “Versatile spectral imaging with an algorithm-based spectrometer using highly tuneable quantum dot infrared photodetectors,” IEEE J. Quantum Electron. 47(2), 190–197 (2011).
[CrossRef]

W.-Y. Jang, M. M. Hayat, J. S. Tyo, R. S. Attaluri, T. E. Vandervelde, Y. D. Sharma, R. Shenoi, A. Stintz, E. R. Cantwell, S. C. Bender, S. J. Lee, S. K. Noh, and S. Krishna, “Demonstration of bias controlled algorithmic tuning of quantum dots in a well (DWELL) MidIR detectors,” IEEE J. Quantum Electron. 45(6), 674–683 (2009).
[CrossRef]

Keating, A. J.

C. A. Musca, J. Antoszewski, K. J. Winchester, A. J. Keating, T. Nguyen, K. K. M. B. D. Silva, J. M. Dell, L. Faraone, P. Mitra, J. D. Beck, M. R. Skokan, and J. E. Robinson, “Monolithic integration of an infrared photon detector with a MEMS-based tunable filter,” IEEE Electron Dev. Lett. 26(12), 888–890 (2005).
[CrossRef]

Kjersti Engan,

S. F. Cotter, B. D. Rao, Kjersti Engan, and K. Kreutz-Delgado, “Sparse Solutions to Linear Inverse Problems With Multiple Measurement Vectors,” IEEE Trans. Signal Process. 53(7), 2477–2488 (2005).
[CrossRef]

Kreutz-Delgado, K.

S. F. Cotter, B. D. Rao, Kjersti Engan, and K. Kreutz-Delgado, “Sparse Solutions to Linear Inverse Problems With Multiple Measurement Vectors,” IEEE Trans. Signal Process. 53(7), 2477–2488 (2005).
[CrossRef]

Krishna, S.

P. Vines, C. H. Tan, J. P. R. David, R. S. Attaluri, T. E. Vandervelde, S. Krishna, W.-Y. Jang, and M. M. Hayat, “Versatile spectral imaging with an algorithm-based spectrometer using highly tuneable quantum dot infrared photodetectors,” IEEE J. Quantum Electron. 47(2), 190–197 (2011).
[CrossRef]

B. S. Paskaleva, W.-Y. Jang, S. C. Bender, Y. D. Sharma, S. Krishna, and M. M. Hayat, “Multispectral classification with bias-tunable quantum dots-in-a-well focal plane arrays,” IEEE Sens. J. 11(6), 1342–1351 (2011).
[CrossRef]

W.-Y. Jang, M. M. Hayat, J. S. Tyo, R. S. Attaluri, T. E. Vandervelde, Y. D. Sharma, R. Shenoi, A. Stintz, E. R. Cantwell, S. C. Bender, S. J. Lee, S. K. Noh, and S. Krishna, “Demonstration of bias controlled algorithmic tuning of quantum dots in a well (DWELL) MidIR detectors,” IEEE J. Quantum Electron. 45(6), 674–683 (2009).
[CrossRef]

B. Paskaleva, M. M. Hayat, Z. Wang, J. S. Tyo, and S. Krishna, “Canonical correlation feature selection for sensors with overlapping bands: theory and application,” IEEE Trans. Geosci. Rem. Sens. 46(10), 3346–3358 (2008).
[CrossRef]

Ü. Sakoğlu, M. M. Hayat, J. S. Tyo, P. Dowd, S. Annamalai, K. T. Posani, and S. Krishna, “Statistical adaptive sensing by detectors with spectrally overlapping bands,” Appl. Opt. 45(28), 7224–7234 (2006).
[CrossRef] [PubMed]

S. Krishna, “Quantum dots-in-a-well infrared photodetectors,” J. Phys. D Appl. Phys. 38(13), 2142–2150 (2005).
[CrossRef]

Ü. Sakoğlu, J. S. Tyo, M. M. Hayat, S. Raghavan, and S. Krishna, “Spectrally adaptive infrared photodetectors with bias-tunable quantum dots,” J. Opt. Soc. Am. B 21(1), 7–17 (2004).
[CrossRef]

S. Krishna, S. Raghavan, G. von Winckel, A. Stintz, G. Ariyawansa, S. G. Matsik, and A. G. U. Perera, “Three-color (λp1~3.8μm, λp2~8.5μm, λp3~23.2μm) InAs/InGaAs quantum-dots-in-a-well detector,” Appl. Phys. Lett. 83(14), 2745–2747 (2003).
[CrossRef]

Lee, S. J.

W.-Y. Jang, M. M. Hayat, J. S. Tyo, R. S. Attaluri, T. E. Vandervelde, Y. D. Sharma, R. Shenoi, A. Stintz, E. R. Cantwell, S. C. Bender, S. J. Lee, S. K. Noh, and S. Krishna, “Demonstration of bias controlled algorithmic tuning of quantum dots in a well (DWELL) MidIR detectors,” IEEE J. Quantum Electron. 45(6), 674–683 (2009).
[CrossRef]

Levine, B. F.

B. F. Levine, “Quantum-well infrared photodetectors,” J. Appl. Phys. 74(8), R1–R81 (1993).
[CrossRef]

Lyon, S. A.

K. W. Berryman, S. A. Lyon, and M. Segev, “Mid-infrared photoconductivity in InAs quantum dots,” Appl. Phys. Lett. 70(14), 1861 (1997).
[CrossRef]

Madhukar, A.

J. C. Campbell and A. Madhukar, “Quantum-dot infrared photodetectors,” Proc. IEEE 95(9), 1815–1827 (2007).
[CrossRef]

Majumdar, A.

A. Majumdar, K. K. Choi, J. L. Reno, L. P. Rokhinson, and D. C. Tsui, “Two-color quantum-well infrared photodetector with voltage tunable peaks,” Appl. Phys. Lett. 80(5), 707–709 (2002).
[CrossRef]

Mallat, S.

G. Davis, S. Mallat, and M. Avellandeda, “Adaptive greedy approximations,” Constr. Approx. 13(1), 57–98 (1997).

Matsik, S. G.

S. Krishna, S. Raghavan, G. von Winckel, A. Stintz, G. Ariyawansa, S. G. Matsik, and A. G. U. Perera, “Three-color (λp1~3.8μm, λp2~8.5μm, λp3~23.2μm) InAs/InGaAs quantum-dots-in-a-well detector,” Appl. Phys. Lett. 83(14), 2745–2747 (2003).
[CrossRef]

Miller, D. A. B.

D. A. B. Miller, D. S. Chemla, T. C. Damen, A. C. Gossard, W. Wiegmann, T. H. Wood, and C. A. Burrus, “Band-edge electroabsorption in quantum well structures: The quantum-confined stark effect,” Phys. Rev. Lett. 53(22), 2173–2176 (1984).
[CrossRef]

Mitra, P.

C. A. Musca, J. Antoszewski, K. J. Winchester, A. J. Keating, T. Nguyen, K. K. M. B. D. Silva, J. M. Dell, L. Faraone, P. Mitra, J. D. Beck, M. R. Skokan, and J. E. Robinson, “Monolithic integration of an infrared photon detector with a MEMS-based tunable filter,” IEEE Electron Dev. Lett. 26(12), 888–890 (2005).
[CrossRef]

Musca, C. A.

C. A. Musca, J. Antoszewski, K. J. Winchester, A. J. Keating, T. Nguyen, K. K. M. B. D. Silva, J. M. Dell, L. Faraone, P. Mitra, J. D. Beck, M. R. Skokan, and J. E. Robinson, “Monolithic integration of an infrared photon detector with a MEMS-based tunable filter,” IEEE Electron Dev. Lett. 26(12), 888–890 (2005).
[CrossRef]

Nguyen, T.

C. A. Musca, J. Antoszewski, K. J. Winchester, A. J. Keating, T. Nguyen, K. K. M. B. D. Silva, J. M. Dell, L. Faraone, P. Mitra, J. D. Beck, M. R. Skokan, and J. E. Robinson, “Monolithic integration of an infrared photon detector with a MEMS-based tunable filter,” IEEE Electron Dev. Lett. 26(12), 888–890 (2005).
[CrossRef]

Noh, S. K.

W.-Y. Jang, M. M. Hayat, J. S. Tyo, R. S. Attaluri, T. E. Vandervelde, Y. D. Sharma, R. Shenoi, A. Stintz, E. R. Cantwell, S. C. Bender, S. J. Lee, S. K. Noh, and S. Krishna, “Demonstration of bias controlled algorithmic tuning of quantum dots in a well (DWELL) MidIR detectors,” IEEE J. Quantum Electron. 45(6), 674–683 (2009).
[CrossRef]

Nölscher, U.

Paskaleva, B.

B. Paskaleva, M. M. Hayat, Z. Wang, J. S. Tyo, and S. Krishna, “Canonical correlation feature selection for sensors with overlapping bands: theory and application,” IEEE Trans. Geosci. Rem. Sens. 46(10), 3346–3358 (2008).
[CrossRef]

Paskaleva, B. S.

B. S. Paskaleva, W.-Y. Jang, S. C. Bender, Y. D. Sharma, S. Krishna, and M. M. Hayat, “Multispectral classification with bias-tunable quantum dots-in-a-well focal plane arrays,” IEEE Sens. J. 11(6), 1342–1351 (2011).
[CrossRef]

Perera, A. G. U.

S. Krishna, S. Raghavan, G. von Winckel, A. Stintz, G. Ariyawansa, S. G. Matsik, and A. G. U. Perera, “Three-color (λp1~3.8μm, λp2~8.5μm, λp3~23.2μm) InAs/InGaAs quantum-dots-in-a-well detector,” Appl. Phys. Lett. 83(14), 2745–2747 (2003).
[CrossRef]

Posani, K. T.

Raghavan, S.

Ü. Sakoğlu, J. S. Tyo, M. M. Hayat, S. Raghavan, and S. Krishna, “Spectrally adaptive infrared photodetectors with bias-tunable quantum dots,” J. Opt. Soc. Am. B 21(1), 7–17 (2004).
[CrossRef]

S. Krishna, S. Raghavan, G. von Winckel, A. Stintz, G. Ariyawansa, S. G. Matsik, and A. G. U. Perera, “Three-color (λp1~3.8μm, λp2~8.5μm, λp3~23.2μm) InAs/InGaAs quantum-dots-in-a-well detector,” Appl. Phys. Lett. 83(14), 2745–2747 (2003).
[CrossRef]

Rao, B. D.

S. F. Cotter, B. D. Rao, Kjersti Engan, and K. Kreutz-Delgado, “Sparse Solutions to Linear Inverse Problems With Multiple Measurement Vectors,” IEEE Trans. Signal Process. 53(7), 2477–2488 (2005).
[CrossRef]

Reno, J. L.

A. Majumdar, K. K. Choi, J. L. Reno, L. P. Rokhinson, and D. C. Tsui, “Two-color quantum-well infrared photodetector with voltage tunable peaks,” Appl. Phys. Lett. 80(5), 707–709 (2002).
[CrossRef]

Robinson, J. E.

C. A. Musca, J. Antoszewski, K. J. Winchester, A. J. Keating, T. Nguyen, K. K. M. B. D. Silva, J. M. Dell, L. Faraone, P. Mitra, J. D. Beck, M. R. Skokan, and J. E. Robinson, “Monolithic integration of an infrared photon detector with a MEMS-based tunable filter,” IEEE Electron Dev. Lett. 26(12), 888–890 (2005).
[CrossRef]

Rokhinson, L. P.

A. Majumdar, K. K. Choi, J. L. Reno, L. P. Rokhinson, and D. C. Tsui, “Two-color quantum-well infrared photodetector with voltage tunable peaks,” Appl. Phys. Lett. 80(5), 707–709 (2002).
[CrossRef]

Sakoglu, Ü.

Segev, M.

K. W. Berryman, S. A. Lyon, and M. Segev, “Mid-infrared photoconductivity in InAs quantum dots,” Appl. Phys. Lett. 70(14), 1861 (1997).
[CrossRef]

Sharma, Y. D.

B. S. Paskaleva, W.-Y. Jang, S. C. Bender, Y. D. Sharma, S. Krishna, and M. M. Hayat, “Multispectral classification with bias-tunable quantum dots-in-a-well focal plane arrays,” IEEE Sens. J. 11(6), 1342–1351 (2011).
[CrossRef]

W.-Y. Jang, M. M. Hayat, J. S. Tyo, R. S. Attaluri, T. E. Vandervelde, Y. D. Sharma, R. Shenoi, A. Stintz, E. R. Cantwell, S. C. Bender, S. J. Lee, S. K. Noh, and S. Krishna, “Demonstration of bias controlled algorithmic tuning of quantum dots in a well (DWELL) MidIR detectors,” IEEE J. Quantum Electron. 45(6), 674–683 (2009).
[CrossRef]

Shenoi, R.

W.-Y. Jang, M. M. Hayat, J. S. Tyo, R. S. Attaluri, T. E. Vandervelde, Y. D. Sharma, R. Shenoi, A. Stintz, E. R. Cantwell, S. C. Bender, S. J. Lee, S. K. Noh, and S. Krishna, “Demonstration of bias controlled algorithmic tuning of quantum dots in a well (DWELL) MidIR detectors,” IEEE J. Quantum Electron. 45(6), 674–683 (2009).
[CrossRef]

Silva, K. K. M. B. D.

C. A. Musca, J. Antoszewski, K. J. Winchester, A. J. Keating, T. Nguyen, K. K. M. B. D. Silva, J. M. Dell, L. Faraone, P. Mitra, J. D. Beck, M. R. Skokan, and J. E. Robinson, “Monolithic integration of an infrared photon detector with a MEMS-based tunable filter,” IEEE Electron Dev. Lett. 26(12), 888–890 (2005).
[CrossRef]

Skokan, M. R.

C. A. Musca, J. Antoszewski, K. J. Winchester, A. J. Keating, T. Nguyen, K. K. M. B. D. Silva, J. M. Dell, L. Faraone, P. Mitra, J. D. Beck, M. R. Skokan, and J. E. Robinson, “Monolithic integration of an infrared photon detector with a MEMS-based tunable filter,” IEEE Electron Dev. Lett. 26(12), 888–890 (2005).
[CrossRef]

Stintz, A.

W.-Y. Jang, M. M. Hayat, J. S. Tyo, R. S. Attaluri, T. E. Vandervelde, Y. D. Sharma, R. Shenoi, A. Stintz, E. R. Cantwell, S. C. Bender, S. J. Lee, S. K. Noh, and S. Krishna, “Demonstration of bias controlled algorithmic tuning of quantum dots in a well (DWELL) MidIR detectors,” IEEE J. Quantum Electron. 45(6), 674–683 (2009).
[CrossRef]

S. Krishna, S. Raghavan, G. von Winckel, A. Stintz, G. Ariyawansa, S. G. Matsik, and A. G. U. Perera, “Three-color (λp1~3.8μm, λp2~8.5μm, λp3~23.2μm) InAs/InGaAs quantum-dots-in-a-well detector,” Appl. Phys. Lett. 83(14), 2745–2747 (2003).
[CrossRef]

Streibl, N.

Tan, C. H.

P. Vines, C. H. Tan, J. P. R. David, R. S. Attaluri, T. E. Vandervelde, S. Krishna, W.-Y. Jang, and M. M. Hayat, “Versatile spectral imaging with an algorithm-based spectrometer using highly tuneable quantum dot infrared photodetectors,” IEEE J. Quantum Electron. 47(2), 190–197 (2011).
[CrossRef]

Tezcan, D.

D. Tezcan, S. Eminoglu, and T. Akin, “A Low-Cost Uncooled Infrared Microbolometer Detector in Standard CMOS Technology,” IEEE Trans. Electron. Dev. 50(2), 494–502 (2003).
[CrossRef]

Tsui, D. C.

A. Majumdar, K. K. Choi, J. L. Reno, L. P. Rokhinson, and D. C. Tsui, “Two-color quantum-well infrared photodetector with voltage tunable peaks,” Appl. Phys. Lett. 80(5), 707–709 (2002).
[CrossRef]

Tyo, J. S.

W.-Y. Jang, M. M. Hayat, J. S. Tyo, R. S. Attaluri, T. E. Vandervelde, Y. D. Sharma, R. Shenoi, A. Stintz, E. R. Cantwell, S. C. Bender, S. J. Lee, S. K. Noh, and S. Krishna, “Demonstration of bias controlled algorithmic tuning of quantum dots in a well (DWELL) MidIR detectors,” IEEE J. Quantum Electron. 45(6), 674–683 (2009).
[CrossRef]

B. Paskaleva, M. M. Hayat, Z. Wang, J. S. Tyo, and S. Krishna, “Canonical correlation feature selection for sensors with overlapping bands: theory and application,” IEEE Trans. Geosci. Rem. Sens. 46(10), 3346–3358 (2008).
[CrossRef]

Ü. Sakoğlu, M. M. Hayat, J. S. Tyo, P. Dowd, S. Annamalai, K. T. Posani, and S. Krishna, “Statistical adaptive sensing by detectors with spectrally overlapping bands,” Appl. Opt. 45(28), 7224–7234 (2006).
[CrossRef] [PubMed]

Ü. Sakoğlu, J. S. Tyo, M. M. Hayat, S. Raghavan, and S. Krishna, “Spectrally adaptive infrared photodetectors with bias-tunable quantum dots,” J. Opt. Soc. Am. B 21(1), 7–17 (2004).
[CrossRef]

Vandervelde, T. E.

P. Vines, C. H. Tan, J. P. R. David, R. S. Attaluri, T. E. Vandervelde, S. Krishna, W.-Y. Jang, and M. M. Hayat, “Versatile spectral imaging with an algorithm-based spectrometer using highly tuneable quantum dot infrared photodetectors,” IEEE J. Quantum Electron. 47(2), 190–197 (2011).
[CrossRef]

W.-Y. Jang, M. M. Hayat, J. S. Tyo, R. S. Attaluri, T. E. Vandervelde, Y. D. Sharma, R. Shenoi, A. Stintz, E. R. Cantwell, S. C. Bender, S. J. Lee, S. K. Noh, and S. Krishna, “Demonstration of bias controlled algorithmic tuning of quantum dots in a well (DWELL) MidIR detectors,” IEEE J. Quantum Electron. 45(6), 674–683 (2009).
[CrossRef]

Vines, P.

P. Vines, C. H. Tan, J. P. R. David, R. S. Attaluri, T. E. Vandervelde, S. Krishna, W.-Y. Jang, and M. M. Hayat, “Versatile spectral imaging with an algorithm-based spectrometer using highly tuneable quantum dot infrared photodetectors,” IEEE J. Quantum Electron. 47(2), 190–197 (2011).
[CrossRef]

von Winckel, G.

S. Krishna, S. Raghavan, G. von Winckel, A. Stintz, G. Ariyawansa, S. G. Matsik, and A. G. U. Perera, “Three-color (λp1~3.8μm, λp2~8.5μm, λp3~23.2μm) InAs/InGaAs quantum-dots-in-a-well detector,” Appl. Phys. Lett. 83(14), 2745–2747 (2003).
[CrossRef]

Walker, S.

Wang, Z.

B. Paskaleva, M. M. Hayat, Z. Wang, J. S. Tyo, and S. Krishna, “Canonical correlation feature selection for sensors with overlapping bands: theory and application,” IEEE Trans. Geosci. Rem. Sens. 46(10), 3346–3358 (2008).
[CrossRef]

Wiegmann, W.

D. A. B. Miller, D. S. Chemla, T. C. Damen, A. C. Gossard, W. Wiegmann, T. H. Wood, and C. A. Burrus, “Band-edge electroabsorption in quantum well structures: The quantum-confined stark effect,” Phys. Rev. Lett. 53(22), 2173–2176 (1984).
[CrossRef]

Winchester, K. J.

C. A. Musca, J. Antoszewski, K. J. Winchester, A. J. Keating, T. Nguyen, K. K. M. B. D. Silva, J. M. Dell, L. Faraone, P. Mitra, J. D. Beck, M. R. Skokan, and J. E. Robinson, “Monolithic integration of an infrared photon detector with a MEMS-based tunable filter,” IEEE Electron Dev. Lett. 26(12), 888–890 (2005).
[CrossRef]

Wood, T. H.

D. A. B. Miller, D. S. Chemla, T. C. Damen, A. C. Gossard, W. Wiegmann, T. H. Wood, and C. A. Burrus, “Band-edge electroabsorption in quantum well structures: The quantum-confined stark effect,” Phys. Rev. Lett. 53(22), 2173–2176 (1984).
[CrossRef]

Appl. Opt.

Appl. Phys. Lett.

K. W. Berryman, S. A. Lyon, and M. Segev, “Mid-infrared photoconductivity in InAs quantum dots,” Appl. Phys. Lett. 70(14), 1861 (1997).
[CrossRef]

S. Krishna, S. Raghavan, G. von Winckel, A. Stintz, G. Ariyawansa, S. G. Matsik, and A. G. U. Perera, “Three-color (λp1~3.8μm, λp2~8.5μm, λp3~23.2μm) InAs/InGaAs quantum-dots-in-a-well detector,” Appl. Phys. Lett. 83(14), 2745–2747 (2003).
[CrossRef]

A. Majumdar, K. K. Choi, J. L. Reno, L. P. Rokhinson, and D. C. Tsui, “Two-color quantum-well infrared photodetector with voltage tunable peaks,” Appl. Phys. Lett. 80(5), 707–709 (2002).
[CrossRef]

Constr. Approx.

G. Davis, S. Mallat, and M. Avellandeda, “Adaptive greedy approximations,” Constr. Approx. 13(1), 57–98 (1997).

IEEE Electron Dev. Lett.

C. A. Musca, J. Antoszewski, K. J. Winchester, A. J. Keating, T. Nguyen, K. K. M. B. D. Silva, J. M. Dell, L. Faraone, P. Mitra, J. D. Beck, M. R. Skokan, and J. E. Robinson, “Monolithic integration of an infrared photon detector with a MEMS-based tunable filter,” IEEE Electron Dev. Lett. 26(12), 888–890 (2005).
[CrossRef]

IEEE J. Quantum Electron.

W.-Y. Jang, M. M. Hayat, J. S. Tyo, R. S. Attaluri, T. E. Vandervelde, Y. D. Sharma, R. Shenoi, A. Stintz, E. R. Cantwell, S. C. Bender, S. J. Lee, S. K. Noh, and S. Krishna, “Demonstration of bias controlled algorithmic tuning of quantum dots in a well (DWELL) MidIR detectors,” IEEE J. Quantum Electron. 45(6), 674–683 (2009).
[CrossRef]

P. Vines, C. H. Tan, J. P. R. David, R. S. Attaluri, T. E. Vandervelde, S. Krishna, W.-Y. Jang, and M. M. Hayat, “Versatile spectral imaging with an algorithm-based spectrometer using highly tuneable quantum dot infrared photodetectors,” IEEE J. Quantum Electron. 47(2), 190–197 (2011).
[CrossRef]

IEEE Sens. J.

B. S. Paskaleva, W.-Y. Jang, S. C. Bender, Y. D. Sharma, S. Krishna, and M. M. Hayat, “Multispectral classification with bias-tunable quantum dots-in-a-well focal plane arrays,” IEEE Sens. J. 11(6), 1342–1351 (2011).
[CrossRef]

IEEE Trans. Electron. Dev.

D. Tezcan, S. Eminoglu, and T. Akin, “A Low-Cost Uncooled Infrared Microbolometer Detector in Standard CMOS Technology,” IEEE Trans. Electron. Dev. 50(2), 494–502 (2003).
[CrossRef]

IEEE Trans. Geosci. Rem. Sens.

B. Paskaleva, M. M. Hayat, Z. Wang, J. S. Tyo, and S. Krishna, “Canonical correlation feature selection for sensors with overlapping bands: theory and application,” IEEE Trans. Geosci. Rem. Sens. 46(10), 3346–3358 (2008).
[CrossRef]

IEEE Trans. Signal Process.

S. F. Cotter, B. D. Rao, Kjersti Engan, and K. Kreutz-Delgado, “Sparse Solutions to Linear Inverse Problems With Multiple Measurement Vectors,” IEEE Trans. Signal Process. 53(7), 2477–2488 (2005).
[CrossRef]

J. Appl. Phys.

B. F. Levine, “Quantum-well infrared photodetectors,” J. Appl. Phys. 74(8), R1–R81 (1993).
[CrossRef]

J. Opt. Soc. Am. B

J. Phys. D Appl. Phys.

S. Krishna, “Quantum dots-in-a-well infrared photodetectors,” J. Phys. D Appl. Phys. 38(13), 2142–2150 (2005).
[CrossRef]

Opt. Eng.

N. Gupta, R. Dahmani, and S. Choy, “Acousto-optic tunable filter based visible- to near-infrared spectropolarimetric imager,” Opt. Eng. 41(5), 1033–1038 (2002).
[CrossRef]

Phys. Rev. Lett.

D. A. B. Miller, D. S. Chemla, T. C. Damen, A. C. Gossard, W. Wiegmann, T. H. Wood, and C. A. Burrus, “Band-edge electroabsorption in quantum well structures: The quantum-confined stark effect,” Phys. Rev. Lett. 53(22), 2173–2176 (1984).
[CrossRef]

Proc. IEEE

J. C. Campbell and A. Madhukar, “Quantum-dot infrared photodetectors,” Proc. IEEE 95(9), 1815–1827 (2007).
[CrossRef]

Other

S. Krishna, M. M. Hayat, J. S. Tyo, S. Raghvan, and Ü. Sakoğlu, “Detector with tunable spectral response,” U.S. Patent 7 217 951, 2007.

W.-Y. Jang, B. Paskaleva, M. M. Hayat, and S. Krishna, “Spectrally adaptive nanoscale quantum dot sensors,” Wiley Handbook of Science and Technology for Homeland Security (Wiley, 2009).

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Figures (11)

Fig. 1
Fig. 1

Bias-tunable spectral bands of a DWELL photodetector for various applied bias voltages in the range −3 to 3 V.

Fig. 2
Fig. 2

Example of three different narrowband tuning filter approximations centered at (a) 7.4 μm, (b) 8.8 μm and (c) 10.2 μm, the algorithm requires 21 out of 30 biases. The biases used are {-3.0, −2.8, −2.6, −2.2, −2.0, −1.8, −1.6, −1.4, −1.2, −0.8, −0.6, −0.4, −0.2, 0.2, 0.4, 0.6, 0.8, 1.4, 1.8, 2.4, 2.6}.

Fig. 3
Fig. 3

Illustration of the remote-sensing applications of data compressive UCSS algorithm.

Fig. 4
Fig. 4

The MBS algorithm is used to approximate the specified spectral-filter collection F MS: (a) f 1(λ), f 2(λ) and f 3 (λ) are hypothetical narrowband triangular sensing filters and (b) f 4(λ), f 5(λ) and f 6(λ) are spectral matched filters using only minimal four biases B MS out of K = 30 biases, B DWELL. The successful approximations using minimal four biases are shown in blue, which corresponds to the error metric P ( b min ) = 6.7% as compared to the approximations using all 30 biases shown in red. The approximations (in blue) of two superposition filters, the spectral integrator f ˜ 1 ( λ ) and the spectral differentiator f ˜ 2 ( λ ) , are shown in (c) along with the approximations using all 30 biases in red.

Fig. 5
Fig. 5

The histogram illustrates the significance of each bias member in the set of 10 biases. By visual inspection, we identified four distinct bias groups.

Fig. 6
Fig. 6

Similarity of the DWELL’s spectral responses at 2.6, 2.8 and 3V.

Fig. 7
Fig. 7

Three spectral filters, f 1(λ), f 2(λ) and f 3 (λ) in the filter collection F MS are used to sample the unknown target, whose transmittance is shown in red. For reference, the ideal triangular spectral filters are also shown in dashed line. Approximated filters in blue line were obtained by the UCSS algorithm using minimum four biases −3.0, −0.8, 1.0, 2.8 V selected by the MBS algorithm.

Fig. 8
Fig. 8

Experimentally reconstructed transmittances (blue circle) at 7.4 μm, 8.8 μm and 10.2 μm extracted by the UCSS algorithm using minimum four biases −3.0, −0.8, 1.0, 2.8 V selected by the MBS algorithm were obtained. Results are compared to the sampled transmittances by the ideal triangular spectral filters (red square) considered as the reference.

Fig. 9
Fig. 9

Applications of two linearly superpositioned filters (i.e., (a) the spectral integrator f ˜ 1 ( λ and (b) the spectral differentiator f ˜ 2 ( λ ) ) to the spectrometry problem of unknown filter target. Approximations f ˜ ^ 1 ( λ ) and f ˜ ^ 2 ( λ ) can extract the spectral average and slope of unknown target, respectively.

Fig. 10
Fig. 10

Classification results for identifying three experimental test data, (I)class. The classifier has successfully assigned the data to Class-1 (see (a)), the data to Class-2 (see (b)), and the data to Class-3 (see (c)).

Fig. 11
Fig. 11

Comparison of classification results for minimal four biases (white) to other bias selections: best-five biases (gray), best-six biases (blue) and all 30 biases (green) for identifying the three experimental test data, (I)class to (a) Class-1, (b) Class-2 and (c) Class-3. Note that the use of minimum four biases obtained by the MBS algorithm in the UCSS algorithm achieved almost identical result compared to the case using all 30 biases.

Tables (6)

Tables Icon

Table 1 Summary of Results for Case (i) Comparing between MBS and AMBS Algorithms for the Approximations of F MS

Tables Icon

Table 2 Summary of Results for Case (ii), Comparing between MBS and AMBS Algorithms for the Approximations F MS

Tables Icon

Table 3 Summary of Results for Case (iii), Comparing between MBS and AMBS Algorithms for the Approximations of F MS

Tables Icon

Table 4 Identified Members in Four Bias Groups for Approximating The Specified Filter Collection F MS*

Tables Icon

Table 5 Comparison of Experimental Reconstruction of the Transmittance at Three Wavelengths Using the Minimal Four Biases by the MBS Algorithm and the Associated Reconstruction Errors to those Using other Bias Selections by the MBS Algorithm (Best-5 Biases, Best-6 Biases and all 30 Biases)

Tables Icon

Table 6 Experimentally Extracted Averaged Transmittance Captured by f ˜ ^ 1 ( λ ) and Slope of Transmittance Captured by f ˜ ^ 2 ( λ ) for Different Bias Selections: Minimum Four Biases, Best-Five Biases, Best-Six Biases and All 30 Biases*

Equations (8)

Equations on this page are rendered with MathJax. Learn more.

i m = λ min λ max p ( λ ) R m ( λ ) d λ + N m ,
w n = [ A T A + Φ + α A T Q T Q A ] 1 [ A T · r ( λ , λ n ) ] ,
w i ( b ) = [ ( A ( b ) ) T A ( b ) + Φ ( b ) + α ( A ( b ) ) T Q T Q A ( b ) ] 1 [ ( A ( b ) ) T f i ( λ ) ] ,         i = 1 , , M .  
e b = 100 × M 1 i = 1 M λ min λ max ( f i ( λ ) f ^ i ( b ) ( λ ) ) 2 d λ λ min λ max f i 2 ( λ ) d λ   .
P ( b ) =   100 ×   | e b e { 1 ,     , K } | .
w ˜ ( b min ) =   i = 1 M β i w i ( b min )
w ˜ ( b min ) =   i = 1 M β i [ ( A ( b min ) ) T A ( b min ) + Φ ( b min ) + α ( A ( b min ) ) T Q T QA ( b min ) ] 1 [ ( A ( b min ) ) T f i ( λ ) ] ,
f ˜ ^ ( λ ) = j = 1 | b min | ( w i ( b min ) ) j R j ( λ ) .

Metrics