Data compressive paradigm for multispectral sensing using tunable DWELL mid-infrared detectors

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.

Plasmonic Superpixel Sensor for Compressive Spectral Sensing

“…Delays in transmission and the storage of redundant spectral data could prove costly for time-sensitive missions or operations. The search for a more efficient sensing method is underway, and efforts to program HS sensors to sort through data on their own and transmit only the most relevant information are being pursued [6].…”

An intelligent readout circuit for infrared multispectral remote sensing

“…Figure 4 illustrates an example of bias waveforms on the four heterogeneous resonators, where its recipe is produced for a given application (e.g. classification) by the algorithm, for instance, proposed by Jang et al [12]. The magnitude of bias controls the photocurrent at wavelength i, Ii, and the duration of bias, Ti, controls the weight of the i th component as shown in (1).…”

A novel readout circuit for on-sensor multispectral classification

“…The spectral-tuning algorithm reported in [2] aimed to address the spectral overlap by forming a weighted superposition of photocurrents, obtained from using different biases, that optimally estimates (in the least-square sense) the ideal narrow-band photocurrent that would be obtained if we were to use a broadband detector to probe the same target of interest through a physical spectral filter having the desired narrow band. Moreover, our group developed a refinement of the algorithm that identifies a minimal set of only four biases to enable sensing only the relevant spectral information for specific remote-sensing applications of interest [4]. photodetector showing potential for multispectral sensing.…”

“…Pictures adopted from [2] For the purpose of this paper, the application of interest is the classification of three types of rocks: granite, hornfels and limestone. From our previous results [4], we know that these rocks can be correctly classified by computing the synthesized feature vector, which is the linear combination of the incoming test photocurrent with the optimal pre-computed set of weights (one for each rock type). Since the set of weights are optimally matched to the spectra of each rock type, the feature component with the maximum value is the assigned class [4].…”

“…From our previous results [4], we know that these rocks can be correctly classified by computing the synthesized feature vector, which is the linear combination of the incoming test photocurrent with the optimal pre-computed set of weights (one for each rock type). Since the set of weights are optimally matched to the spectra of each rock type, the feature component with the maximum value is the assigned class [4]. Based on our previous results, the minimal set of bias voltages is {-3.0, -0.8, 1.0, 2.8} volts, and the three weight vectors (one for each rock type) are W1 = [15, -109, 32, 10], W2 = [24, -63, -5, -8] and W3 = [11, 3, -128, 24] [5].…”