Imagine a pen-sized device to check your skin for melanoma. You skim the surface
of your skin, and, if necessary, the pen advises you to see your physician to
have a closer look at a certain spot. Such a pen would scan your skin, and detect
if skin cancer is developing, even in an early stage. It would distinguish between
healthy and suspicious spots, even if you can see no difference.
The technology to detect what our eyes can’t see has been available for
more than a decade. Called hyperspectral imaging (HSI), it enables to collect
and process information from across the electromagnetic spectrum. This is unlike
the human eye, which just sees the narrow spectrum of visible light. Every surface
and material gives off a specific HSI fingerprint. This enables to distinguish
between surfaces that look totally alike to the human eye: regular or fake banknotes,
coffee beans or small stones, healthy skin spots or melanoma.
So today already, there are HSI cameras that can check your skin for melanoma.
However, the machinery that you’d need is bulky and difficult to set up,
and the cost would be prohibiting.
Imec has challenged itself to overcome the
HSI hurdle set by the size, cost, and slow speed of current systems. We want
to enable microsized hyperspectral imaging. HSI that, ultimately, fits into
a pen. We want to make it much faster, so that it can be used to monitor real-time
events. And last, we want to make it so cheap that everyone could afford the
Applications for HSI abound, but the technology isn’t yet up
to the task
The potential applications of HSI abound, and it has been hailed over time
as a revolutionary technique. In food sorting, HSI cameras could scan for unwanted
items (stones, other organic material, unripe or too ripe fruits). The medical
world is also interested in HSI, especially in the property that a HSI camera
can look below the surface of tissues. A HSI scan could, for example, minimize
invasive surgery needed for tissue sampling. Or it can be used to oversee labs
and hospitals for contaminants. In automotive applications, HSI cameras could
be used to scan the condition of the road ahead. HSI can also be used for air
surveillance, looking at tree or crop health, or searching for oil and other
However, the full potential of hyperspectral imaging has not yet been realized.
Current HSI solutions are mainly used in research centers, with a few systems
installed in satellites. They are bulky, expensive optical systems, costing
in the order of 80k USD. Moreover, they are slow, analyzing at most 180 lines
per second. For food sorting, to replace current systems, you’d need to
scan at least 8000 lines per second. And the storage and analysis of the captured
data is even slower, making real-time analysis all but impossible. This is because
hyperspectral images are large multi-dimensional datasets, potentially exceeding
hundreds of megabytes.
The challenge: microsized, superfast, and cheap
What we envision are industrial application-specific tools, with a much smaller,
monolithically integrated design. They should be able to scan more than 8000
lines/second, so that they can be used in food sorting applications. And they
should analyze the scanned data in real time, which will call for application-optimized
State-of-the-art HSI cameras use expensive and bulky optics, taking pictures
through a moving slit, allowing them to analyze line per line. The image of
such a line is then decomposed using optical components such as gratings or
liquid crystal tunable filters. Last, the data of this decomposition are stored
and analyzed. Each component of these cameras is state-of-the-art, but the overall
design is suboptimal in many ways.
Our solution will use Fabry-Pérot filters. These are resonant filters
consisting of 2 mirrors. Depending on the distance between the mirrors, only
light of a certain wavelength passes through the filter. By adapting the distances
in discrete steps, the spectrum may be sampled. These filters can be micromachined
and integrated on an image sensor. Ultimately, we’re envisaging a sensor
where each pixel of the image sensor has its own Fabry-Pérot filter.
This would allow analyzing complete images at a time, instead of lines.
Microsizing will be only part of the solution. Another huge gain in efficiency
will come from co-design: designing system, hardware, software all together
with a view of optimizing each layer with the help of the other layers. An example
from the HSI sensor is the size of the filter steps, where we can use the software
to compensate for larger tolerances. This somewhat relaxes the specs and fabrication
challenges for the Fabry-Pérot filters.
Imec’s research into next-generation vision systems
Hyperspectral imaging is only one example of the vision systems that we have
in the pipeline, either as ongoing R&D or as ideas that will be further
developed in the near future. With our program, we want to create next generation
vision systems by combining our expertise in systems design and multimedia with
our CMORE process technology (www.imec.be/cmore). Precisely with the combination
of that expertise, we see ways to create innovative solutions. Solutions that,
through their added functionality and microsize, will create new applications,
or make existing applications much wider used. Think of the melanoma-pen with
embedded hyperspectral camera and image analysis.