By Dawn Levy

Faster than a speeding bullet. Able to leap photographic obstacles with a
single computer chip. It’s a camera. It’s a chip. It’s a camera-on-a-chip.

Thanks to the efforts of electrical engineering Professor Abbas El Gamal,
psychology and electrical engineering Professor Brian Wandell and their
students, it’s getting harder to take a bad picture. Conventional digital
cameras capture images with sensors and employ multiple chips to process,
compress and store images. But the Stanford researchers have developed
an innovative camera that uses a single chip and pixel-level processing
to accomplish those feats. Their experimental camera-on-a-chip may spawn
commercial still and video cameras with superpowers including perfect
lighting in every pixel, blur-free imaging of moving objects, and
improved stabilization and compression of video.

“The vision is to be able to ultimately combine the sensing, readout,
digitization, memory and processing all on the same chip,” says El Gamal.
“All of a sudden, you’d have a single-chip digital camera which you can
stick in buttons, watches, cell phones, personal digital assistants and
so on.”

Most of today’s digital cameras use charge-coupled device (CCD) sensors
rather than the far less expensive complementary metal-oxide
semiconductor (CMOS) chips used in most computing technologies. Light
arriving at the CCD sensor is converted into a pixel charge array. The
charge array is serially shifted out of the sensor and converted to a
digital image using an analog-to-digital converter. The digital data
are processed and compressed for storage and subsequent display.

Reading the data from a CCD is destructive. “At that point the charge
within the pixel is gone,” Wandell says. “It’s been used in the
conversion process, and there’s no way to continue making measurements
at that pixel. If you read the charge at the wrong moment, either too
soon or too late, the picture will be underexposed or overexposed.”

Another limitation of CCD sensors, El Gamal says, is designers cannot
integrate the sensor with other devices on the same chip. Creating
CMOS chips with special circuitry can solve both of these problems.

In 1993, El Gamal began working on image sensors that led to the
establishment of Stanford’s Programmable Digital Camera Project to
develop architecture and algorithms capable of capturing and processing
images on one CMOS chip. In 1998, he, Wandell and James Gibbons, the
Reid Weaver Dennis Professor of Electrical Engineering, brought a
consortium of companies together to fund their research effort.
Agilent, Canon, Hewlett-Packard and Eastman Kodak currently fund the
project. Founding sponsors included Interval Research and Intel.

Designers of the Mars Polar Lander at NASA’s Jet Propulsion Laboratory
were the first to combine sensors and circuits on the same chip. They
used CMOS chips, which could tolerate space radiation better than CCDs,
and the first-generation camera-on-a-chip was born. It was called the
active pixel sensor, or APS, and both its input and output were analog.

The Stanford project generated the second-generation camera-on-a-chip,
which put an analog-to-digital converter in every pixel, right next
to the photodetector for robust signal conversion. Called the digital
pixel sensor, or DPS, it processed pixel input serially — one bit at
a time.

In 1999, one of El Gamal’s former graduate students, Dave Yang,
licensed DPS technology from Stanford’s Office of Technology Licensing
and founded Pixim, a digital imaging company that aims to embed the
DPS chip in digital still and video cameras, toys, game consoles,
mobile phones and more.

The need for speed

The second-generation camera-on-a-chip was relatively peppy at 60
frames per second. But the third generation left it in the dust,
capturing images at 10,000 frames per second and processing one
billion pixels per second. The Stanford chip breaks the speed limit
of everyday video (about 30 frames per second) and sets a world
speed record for continuous imaging.

What makes it so fast? It processes data in parallel, or
simultaneously — the chip manifestation of the adage “Many hands make
light work.”

“While you’re processing the first image, you’re capturing the econd,”
El Gamal explains. “It’s pipelining.”

Besides being speedy, its processors are small. At a Feb. 5 meeting
of the International Solid State Circuits Conference in San Francisco,
El Gamal and graduate students Stuart Kleinfelder, Suk Hwan Lim and
Xinqiao Liu presented their DPS design employing tiny transistors only
0.18 micron in size. Transistors on the APS chip are twice as big.

“It’s the first 0.18-micron CMOS image sensor in the world,” El Gamal
says. With smaller transistors, chip architects can integrate more
circuitry on a chip, increasing memory and complexity. This
unprecedented small transistor size enabled the researchers to
integrate digital memory into each pixel.

“You are converting an analog memory, which is very slow to read out,
into a digital memory, which can be read extremely fast,” El Gamal
says. “That means that the digital pixel sensor can capture images
very quickly.”

The DPS can capture a blur-free image of a propeller moving at 2,200
revolutions per minute. High-speed input coupled with normal-speed
output gives chips time to measure, re-measure, analyze and process
information. Enhanced image analysis opens the door for new or
improved research applications including motion tracking, pattern
recognition, study of chemical reactions, interpretation of lighting
changes, signal averaging and estimation of three-dimensional

Photography is not a new tool in research. In 1872, Leland Stanford
hired Eadweard Muybridge to conduct photographic experiments testing
his idea that at one point in its gait, a horse has all four feet off
the ground. That research led to the development of motion pictures.

But most people don’t use cameras to advance the frontiers of science
or industry. They just want to take a decent picture, and high-speed
capture solves a huge problem: getting proper exposure throughout an
image with a big range of shades between the darkest and brightest
portions of the picture.

“It’s very difficult to combine low-light parts of an image with
high-light parts of an image in one image,” El Gamal says. “That’s one
of the biggest challenges in photography. Film does a very good job of
that. Digital cameras and video cameras don’t do as well.”

Images taken in bright environments need short film or pixel exposure
times, and those taken in dim environments need long exposure times.
With a single click, the camera-on-a-chip captures and measures the
charges in the pixels repeatedly, at high speed. Its algorithm waits
until the right moment for each individual pixel to assemble a final
picture with perfect exposure throughout the image.

“What I consider the real breakthrough of this [DPS] chip is that it
can do many very fast reads without destroying the data in the sensor,”
Wandell says.

Wandell, an expert in human vision and color perception, says working
with cameras gives him ideas for hypotheses about how the brain
processes images. He and students including Jeffrey DiCarlo and Peter
Catrysse in electrical engineering and Feng Xiao in psychology have
used ideas from human vision to create image-processing algorithms
to optimize the image quality for printing or display on a computer

While current cameras can focus on objects and judge illumination levels,
historically they have not been used for image analysis, Wandell says.
Their forte — image capture — is a “fundamentally different job,” he
says. The human brain’s forte, however, is image analysis, and Wandell
says future camera designs may borrow from biology to build more
intelligence into cameras.