Compressive Current Response Mapping of Photovoltaic Devices Using MEMS Mirror Arrays

Hall, Simon R. G.; Cashmore, Matt; Blackburn, John; Koutsourakis, George; Gottschalg, Ralph

doi:10.1109/tim.2016.2559878

Cited by 14 publications

(12 citation statements)

References 18 publications

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“…Unlike the behavior seen in Ref. 5, where there are sudden jumps, here we observe a rapid increase in the reconstruction accuracy up to approximately one-fifth of the total data points. The correlation then increases slowly, plateauing at a value of ∼95%.…”

Section: Resultscontrasting

confidence: 75%

“…8 It happens that these properties can both be met through forcing the measurement matrix ϕ to contain random entries. 9 Previous results from using Hadamard dictionaries to reconstruct the signal 5 showed that the reconstruction using an l 1 -norm minimization technique 10 did not produce an optimal reconstruction. However, this was determined to be a consequence of the nonsparse descriptions of the spatial response profile in the Hadamard domain.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…It has previously been demonstrated 5 that it is possible to recover the response map of a PV solar cell using fewer measurements than those required using a raster technique such as LBIC. This is achieved using compressive sensing algorithms 6,7 from signal processing theory and the projection of binary illumination patterns based on the Hadamard matrix function.…”

Section: Introductionmentioning

confidence: 99%

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Optical technique for photovoltaic spatial current response measurements using compressive sensing and random binary projections

et al. 2016

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Abstract. Compressive sensing has been widely used in image compression and signal recovery techniques in recent years; however, it has received limited attention in the field of optical measurement. This paper describes the use of compressive sensing for measurements of photovoltaic (PV) solar cells, using fully random sensing matrices, rather than mapping an orthogonal basis set directly. Existing compressive sensing systems optically image the surface of the object under test, this contrasts with the method described, where illumination patterns defined by precalculated sensing matrices, probe PV devices. We discuss the use of spatially modulated light fields to probe a PV sample to produce a photocurrent map of the optical response. This allows for faster measurements than would be possible using traditional translational laser beam induced current techniques. Results produced to a 90% correlation to raster scanned measurements, which can be achieved with under 25% of the conventionally required number of data points. In addition, both crack and spot type defects are detected at resolutions comparable to electroluminescence techniques, with 50% of the number of measurements required for a conventional scan.

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Section: Resultscontrasting

confidence: 75%

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optical technique for photovoltaic spatial current response measurements using compressive sensing and random binary projections

et al. 2016

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“…A primitive "proof of concept" for CS current mapping of PV devices has been introduced in previous work [20] [21]. Instead of applying a raster scan, a series of patterns are projected on the PV sample using a DMD, acquiring fewer measurements (M) than the pixels of the final current map (N).…”

Section: Introductionmentioning

confidence: 99%

Compressed Sensing Current Mapping Spatial Characterization of Photovoltaic Devices

Koutsourakis

Cashmore

Hall

et al. 2017

IEEE J. Photovoltaics

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“…A CS current mapping optical prototype has been built at NPL based on a DMD chip. This initial setup can perform small area scans and initial measurements have proved the feasibility of the method [8] [9]. Current maps have been produced with just 30% of the measurements a standard LBIC system would need.…”

Section: Introductionmentioning

confidence: 99%

Compressed sensing current mapping methods for PV characterisation

Koutsourakis

Cashmore

Bliss

et al. 2016

2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC)

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Compressive Current Response Mapping of Photovoltaic Devices Using MEMS Mirror Arrays

Cited by 14 publications

References 18 publications

Optical technique for photovoltaic spatial current response measurements using compressive sensing and random binary projections

Optical technique for photovoltaic spatial current response measurements using compressive sensing and random binary projections

Compressed Sensing Current Mapping Spatial Characterization of Photovoltaic Devices

Compressed sensing current mapping methods for PV characterisation

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