Hello. I'm Chris Kelly, Applications Engineer with Keysight Technologies.
After spending 33 years in the R&D lab with Hewlett Packard, Agilent, and now Keysight,
I'm here to use my experience to help you solve the problems -- the challenging problems
of low current battery drain analysis in IoT devices.
The Internet of Things is going to be growing rapidly, and the wearables category represent
a very challenging area where the signals are highly dynamic over orders of magnitude
and spans of time, and the battery life has to be extended up into convenient zones for wearables
or, in some cases, to many years for deposited devices.
Keysight offers a number of solutions that are available to attack this problem in different ways.
And as you can see behind me, we have a device current waveform analyzer with a typical pattern
from a wearable device as a device under test.
We also have an oscilloscope, which may already be on your bench, but it's using a current
probe to measure those waveforms and quantify the problem.
We have traditional DMM, which I'm sure most of you are familiar with, but I'm going to
show you properties of DMMs that you may not know about that can help you
with these highly dynamic signals.
And finally, we have a DC power analyzer. Again, purpose-made for this job.
This video is the first part of a series that demonstrates
Keysight solutions for battery drain analysis.
We're going to focus on low power consumption
and those highly variable waveforms of wearable IoTs.
How do you visualize, how do you quantify, and how do you reduce the battery drain in
the different operating modes?
This long battery life is a key feature because users do not want to be charging their IoT
devices all the time, and some IoT devices are going to go out in the field and expect
to be operating for years.
You as a designer have a choice of components, the duration of their operation, how often
they operate, and you can even tune the firmware which controls the power supplies, the data
converters, the radios, and other components of their devices.
These devices do have very small but highly variable battery drain.
That's the challenge.
We're looking at a picture, a concept diagram of a wearable fitness tracker, which in the
center you see is controlled by a CPU, an embedded microprocessor.
On the left, we have a series of inputs: pushbutton, sensors -- anything
that you're measuring with your IoT device.
The CPU in the middle controls the timing of these operations.
And on the right we have the outputs: the display, maybe LED, LCD, a USB port, a radio,
whatever the device uses to communicate with the world.
Sometimes the power supplies for these different devices consume a lot of the battery, and
so you don't want to turn them on all the time.
Finally, at the bottom is the battery itself.
This is our key device.
We need to extend the life of that battery by minimizing the charge in normal operation
of this device.
So, the waveforms we're talking about are quite complex.
As seen on the display of the CX3300 family here, they have amplitude ratios of 200:1 or more.
Keysight offers a wide spectrum of tools that you can use to solve this IoT device measurement problem.
Next to the CX3300 we have an oscilloscope with a current measuring oscilloscope probe.
These turn your oscilloscope, a familiar bench tool, into a scope that can measure
the current at high frequencies.
These scope probes are capable of around 3 MHz and are high resolution in
terms of time.
The very low measurements we're making are challenging because this little battery operated
device needs to have a long battery life.
Next to the scope we have another device, a very familiar device, the DMM.
You probably use this all the time, but what you may not know is that the DMM has digitizing
modes available to it that can look at these waveforms up to about 10 kHz bandwidth.
So, it's not just a static measurement device with very high precision; it's also a dynamic
measurement device.
And finally, you have purpose-made devices for the measurement of battery drain for power
consumption, which is the N6705 with an SMU built in -- a source measure unit -- that,
again, is designed specifically for this problem of measuring power consumption.
In this series of videos, we'll be reviewing the operation of each of the several instruments
we've introduced for battery drain analysis of low power IoT devices.