editor's blog
Subscribe Now

Wide-Ranging Approaches to Ranging

As I’ve mentioned before, there are constants at ISSCC (e.g., sessions on image processing and sensors) and then there are the circuits-of-the-month. Ranging seemed to be one of the latter, showing up in both image-processing and sensor sessions. So I thought I’d summarize some of the widely differing approaches to solving issues related to ranging for a variety of applications.

For those of you following along in the proceedings, these come from sessions 7 and 12.

Session 7.4 (Shizuoka University, Brookman Technology) offered a background-cancelling pixel that can determine the distance of an object using time-of-flight (ToF). As you may recall, ToF is more or less like light radar (LIDAR?) where the arrival of the reflection of a known emitted light gives you the distance.

There are four lateral gates in this pixel, directing charge from impinging light into one of three floating diffusion areas (the fourth gate simply discharges the pixel).

Background cancellation has historically been done by comparing adjacent frames, but quick motion can create strange artifacts. So at the beginning of the capture cycle for this work, the background is measured and stored in the first diffusion for subtraction. Then the emitter turns on and collection moves to the second diffusion. The reflection may also return during that time; when the emitter shuts off, then collection changes to the third diffusion. The difference between those two charge amounts gives the distance.

Session 7.5 (Shisuoka University) addresses the challenge of doing high-precision ranging for the purposes of, say, modeling an object. The problem is that, to get higher resolution, you ordinarily need to separate the light source from the imager by a wide angle. That’s hard to do in a small device. Such devices typically have resolution in the few-cm range, which isn’t much use for object modeling; this work achieved 0.3-mm resolution.

The keys were three:

  • They use an extremely short (< 1 ns) light pulse.
  • They used a drain-only modulator (DOM) – by eliminating the lateral pass gate, they get a faster response. The pixel itself can only accumulate or drain.
  • They capture all of the pixels at once, but the tight timing brings another issue: skew between pixels is no longer noise, but can screw up the measurement. So they implemented a column deskew circuit and procedure.

Microsoft weight in in Session 7.6 (they couldn’t help putting a flashy brand on their opening slide – something that you generally don’t see at ISSCC, but I guess the marketing guys need something to prove their value, even if it meant being tasteless). This was an improved Kinect ranging system where the challenge is in accommodating both distant low-reflectivity (i.e., low-light) and close-in high-reflectivity (i.e., high-light) objects. Pretty much your classic dynamic range issue complicated by the distance thing.

They have decoupled the collection of charge in a floating diffusion and an “A or B” assignment that will be used to calculate the distance. They use A and B rows as inputs to a differential cell. A high-frequency clock alternates A and B activation during collection; this means that the assignment to A or B, determined by the clock, happens simultaneously with charge collection. The transfer to a floating diffusion can then happen afterwards, at a leisurely pace (to use their word).

They also implemented a common-mode reset to neutralize a bright ambient. And each pixel can set its gain and shutter time; this is how they accommodate the wide dynamic range.

Meanwhile, over in Session 12, folks are using other sensors for ranging. In Session 12.1 (UC Berkeley, UC Davis, Chirp Microsystems), they built a pMUT (piezoelectric micro-machined ultrasonic transducer) array to enable gesture recognition. Think of it as phased-array radar on a miniscule scale. They process the received signals by phase-shifting – basically, beamforming – in an attached FPGA.

Within the array, some pMUTs (think of them as ultrasonic pixels, sort of) are actuated to send a signal, others listen to receive the reflection, and some do both. They can decide which of these to do for optimization purposes on a given application.

They also want to sample at 16x the resonant frequency of the sensors to lower in-band quantization noise and simplify the cap sizing. (No relation to an unfortunate boating incident.) But that means they need to know the actual, not approximate, resonant frequency for a given device – natural variation has to be accommodated, as does response to changing environmental conditions like temperature.

To do this, they have a calibration step where they actuate the sensors and measure their ring-down, using the detected frequency to set the drive frequency of the actuator. This calibration isn’t done with each capture; it can be done once per second or minute, as conditions for a given application warrant.

As always, the details on these sessions are in the proceedings.

Leave a Reply

featured blogs
Sep 25, 2020
[From the last episode: We looked at different ways of accessing a single bit in a memory, including the use of multiplexors.] Today we'€™re going to look more specifically at memory cells '€“ these things we'€™ve been calling bit cells. We mentioned that there are many...
Sep 25, 2020
Normally, in May, I'd have been off to Unterschleißheim, a suburb of Munich where historically we've held what used to be called CDNLive EMEA. We renamed this CadenceLIVE Europe and... [[ Click on the title to access the full blog on the Cadence Community site...
Sep 24, 2020
I just saw a video from 2012 in which Jeri Ellsworth is strolling around a Makerfaire flaunting her Commodore 64-based bass guitar....
Sep 24, 2020
Samtec works with system architects in the early stages of their design to create solutions for cable management which provide even distribution of thermal load. Using ultra-low skew twinax cable to route signals over the board is a key performance enabler as signal integrity...

Featured Video

Four Ways to Improve Verification Performance and Throughput

Sponsored by Cadence Design Systems

Learn how to address your growing verification needs. Hear how Cadence Xcelium™ Logic Simulation improves your design’s performance and throughput: improving single-core engine performance, leveraging multi-core simulation, new features, and machine learning-optimized regression technology for up to 5X faster regressions.

Click here for more information about Xcelium Logic Simulation

Featured Paper

4 audio trends transforming the automotive industry

Sponsored by Texas Instruments

The automotive industry is focused on creating a comfortable driving experience – but without compromising fuel efficiency or manufacturing costs. The adoption of these new audio technologies in cars – while requiring major architecture changes – promise to bring a richer driving and in-car communication experience. Discover techniques using microphones, amplifiers, loudspeakers and advanced digital signal processing that help enable the newest trends in automotive audio applications.

Click here to download the whitepaper

Featured Paper

Four Ways to Improve Verification Performance and Throughput

Sponsored by Cadence Design Systems

Learn how to address your growing verification needs. Hear how Cadence Xcelium™ Logic Simulation improves your design’s performance and throughput: improving single-core engine performance, leveraging multi-core simulation, new features, and machine learning-optimized regression technology for up to 5X faster regressions.

Click here for more information about Xcelium Logic Simulation