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Reliable data is fundamental. Even when sitting in a crowded stadium watching U2? YEE HA I got the tickets! Now I need to tell all my loser friends how clever I was to get here. Connect to stadium WiFi – not happening. I log onto facebook...try to post. Still trying. On to Instagram. (I need to put my glasses on…to see what to do) Snapchat? FORGET IT - my 20 yr old son will disown me!

 

I spend the entire concert trying to tell my “non existent” virtual friends what they are missing. By the time I am home, I’m obsessed with checking my likes and replies. A crowded or inadequate network situation is something we all know and don’t love. However, a failing network in critical IoT systems can be disastrous.

 

For IoT, a reliable network is key to success. The vast majority of connected objects will connect back to the cloud wirelessly using RF and microwave frequencies. The ability to operate reliably is especially challenging in an environment such as a factory where there is metal and concrete throughout the facility.

 

Reliable operation needs to encompass everything from low to high data rates, short to long range operating distances or a device situated in a hard to reach area that only needs to communicate when it's absolutely necessary. Take forest fire sensing, when a blaze is detected, a notification needs to happen and quickly. Therefore some devices may go months or years without communicating and others will need to operate continuously across mission critical secure networks. Also many of these sensor nodes will also be self-powered through batteries or energy harvesters so efficient operation is also key to success. The communication networks are critical to transport the intelligence from sensor to cloud across differing requirements.

 

What is needed? Ideally, a technology that is low cost, low power and with low latency. But also with the capability to scale a system with unrestricted sensor placement. One example of a reliable network is creating an implementation by using alternate pathways and channels to overcome interference. If a signal faces potential interference, it simply moves on to another channel rather than risk downtime.

 

And finally, maybe we need to look at the bright side of drop out in crowded social situations; perhaps we should try listening to the music instead of posting about every moment. (I don’t care where you are or how much fun you theoretically are having! I AM NOT JEALOUS!)

 

From Rant to Reality

To learn more about low power, secure wireless networks from ADI

http://www.linear.com/dust_networks/

Want to get a better feel for how a precision SAR ADC performs in a signal chain design before you get your pcb design manufactured?  Tired of having to spend time and re-design effort to get your precision data acquisition design just right? Then give our new AD7960 SAR LTSpice model a try and tell us about your experience in the comments. The models, two test benches and a user guide are attached to this blog in a zip file and may be downloaded below.

The pdf user guide contained in the zip file will lead you through the model, as well as demonstrate how to run the two example test benches and the results you should expect. Please note this is a BETA model. As this is still a work in progress, you may find some bugs but that’s what the comment section is for.

The first test bench simulates a full scale input step to the ADC and driver circuit, similar to what you might see if you were multiplexing many channels into one ADC. The simulation results are shown along with wave forms of the input settling below.

The second test bench shows the AC performance of the ADC and drive circuit and how to perform an FFT in LTSpice. The input to the driver circuit and ADC in this case is a 14.6Khz differential sine wave and we perform 1024 conversions in order to do a 1024 point FFT. The FFT result is shown below.

Feel free to give the model and test benches a try as well as make changes to simulate different scenarios e.g. different driver amplifiers, RC combinations etc. As stated above the model is a BETA model. We welcome any and all feedback regarding what you like, don’t like or what you would like to see added. Please comment below this blog to share your thoughts on our new LTSpice model for the AD7960 Precision SAR ADC.

Welcome back to the ADAQ798x ADC driver configuration blog series! Today, we’ll conclude this series with an overview of the Sallen-Key active low-pass filter topology for the ADAQ798x. This configuration is one of the simpler active filtering implementations, and allows the ADAQ798x to maximize performance even when interfacing with noisy input sources and sensors.

 

Sallen-Key Low-Pass Filter

The Sallen-Key topology can be used to configure the ADAQ798x’s ADC driver as an active, two-pole, low-pass filter. This configuration is relatively simple, since the ADC driver is set in a simple non-inverting configuration, so the filter doesn’t directly impact its performance and bandwidth (see ADI’s Linear Circuit Design Handbook). The implementation of the low-pass filter requires two resistors (R1 and R2) and two capacitors (C1 and C2) to set the filter cut-off, and an optional two resistors (Rf and Rg) to add signal gain:

 

The configuration can be thought of as cascading a -40 dB/decade filter followed by a gain stage:

 

 

The values of R1, R2, C1 and C2 determine the filter’s shape and response. For this blog post we’ll focus on a configuration where R1 = R2 and C1 = C2. This combination results in a filter with a Q factor of 0.5, and behaves similarly to two equivalent RC low-pass filters in series. The frequency response for this case is:

 

Assuming R1 = R2 = R and C1 = C2 = C, the filter corner frequency is given by:

 

At the corner frequency fc, the response of the filter is roughly -6 dB from its dc gain. The dc gain of the filter is given by the non-inverting gain relationship we saw in previous posts:

 

This configuration can reduce out-of-band noise from the signal source, sensor, or other analog front-end circuitry. If these pieces of the signal chain feature significantly more noise than the components included in the ADAQ798x, and the signal bandwidth is small compared to the Nyquist rate of the ADC, then using this configuration can help improve the system noise performance. The rms voltage noise from a source connected to the filter input (vn rms) is:

 

where ein is the noise spectral density from the input source, AV is the gain of the ADC driver (shown above), and fENBW is the effective noise bandwidth of the filter. This assumes that the active filter cutoff frequency is significantly lower than that of the ADAQ798x’s integrated RC filter (which will virtually always be the case). fENBW for the filter described above is simply:

 

The filter cutoff frequency can be selected near the maximum input frequency required for the application to maximize noise reduction. Let’s look at an example to see how this configuration can improve system noise performance.

 

For a system with an input noise spectral density of 500 nV/√Hz, and a signal gain of 1, what would the cutoff frequency (fc) need to be to make sure the input source contributes no more than 100 μV rms noise to the system? Solving for fc in the equation used above gives:

 

Using R1 = R2 = 1.2 kΩ and C1 = C2 = 2.7 nF can be used to achieve a filter cutoff close to this (~49 kHz).

 

Closing Thoughts

Today, we looked at a simple implementation of an active, 2-pole low-pass filter using the ADAQ798x’s integrated ADC driver. This is one of many potential configurations that can be used for achieving active filtering with the ADAQ798x.

 

System noise performance can be further improved by combining active filtering with oversampling and decimation. Oversampling and decimation is a form of digital filtering, where a certain number of consecutive samples are averaged together to reduce out-of-band noise at the expense of signal bandwidth (see this article for more information).

 

One thing to keep in mind when designing an active filter is the flatness of the filter’s pass band. Many filters exhibit some deviation in the pass band, especially if they result in resonance or peaking at a certain frequency. When deciding on and designing an active filter topology, be aware of the application’s required gain flatness across the bandwidth of interest.

 

Thanks again for joining me for this last entry in our blog series about alternate configurations for the ADAQ798x’s integrated ADC driver! Hopefully you’re now equipped to begin taking advantage of the device’s flexible analog front-end for your application!

 

Have any questions? As always, feel free to ask in the comments section below!

Welcome back to the ADAQ798x ADC driver configuration blog series! In today’s post, we’re going to look at the difference amplifier configuration, another means of interfacing the ADAQ798x with bipolar input signals. This configuration can be used for bipolar signals with wide input voltage ranges and bandwidths. We’ll see how to select the required external components for any given input range and how they affect other specifications like input impedance, noise, and dc errors.

 

The Difference Amplifier

The ADC driver can be configured as a difference amplifier using four external resistors, shown below:

 

This configuration can be thought of as a superposition of the non-inverting and inverting configurations; the bipolar input signal is multiplied by the amplifier’s inverting gain, while the dc bias voltage (using VREF, for reasons discussed in previous posts) is multiplied by the non-inverting gain. CN-0393 utilizes this configuration to condition a ±10 V output swing from a PGIA (AD8251). The transfer function for this configuration is:

 

The first step is to find the appropriate ratio of Rf and Rg, which is determined by the ratio of the input amplitude (ΔvIN) to the full-scale range of the ADC (0 V to VREF):

 

Unlike with the non-inverting configurations we’ve discussed, the signal gain can be less than 1, so we don’t need to make any modifications (i.e. additional resistors) to attenuate input signals with amplitude larger than VREF. It’s worth noting that the signal does get inverted from the input to the output.

 

R1 and R2 are then used to attenuate VREF such that the output of the ADC driver is biased to the ADC midscale (VREF/2). The ratio of R1 and R2 is determined by the ratio of Rf and Rg:

 

The above also assumes that the design is utilizing VREF as the dc input voltage tied to R1.

 

After finding these ratios, we then need to select specific values for each of the resistors. There are a few considerations to make before we start blindly selecting components:

 

First, the value of Rf can affect the ADC driver’s stability. If Rf becomes too large, the noise gain frequency response will start peaking, and can become unstable (as described in MT-050). As we mentioned several posts ago in "Adding Gain for Unipolar Inputs", Rf should be limited to prevent this from occurring.

 

Also, as we saw in our previous post, "Attenuating Bipolar Inputs", larger resistors will result in more system noise. This configuration is more susceptible to noise issues than the one we discussed last week, because the ADC driver’s noise gain will always be larger than 1. The Noise Considerations and Signal Settling section in the ADAQ7980/ADAQ7988 data sheet and the System Noise Analysis section in CN-0393 shows how to quantify the system noise for this configuration.

 

And still another consideration is the resistors effect on the system offset error. The resistors will interact with the ADC driver’s input bias current to create an offset error at its output. This effect becomes more pronounced as their resistances increase. According to MT-038, in order to mitigate this effect, the parallel combination of R1 and R2 must be equal to that of Rf and Rg.

 

Let’s consider an example where vIN is ±1.25 V and VREF = 5 V. Using the equations above, we find that Rf must be 2×Rg, and R1 must be 5×R2. If we want to ensure that the input bias currents don’t create system offset error, the parallel combinations of R1||R2 and Rf||Rg must be equal as well, which R1 = 0.8×Rf. If we select Rf = 2 kΩ, for example, we need Rg = 1 kΩ, R1 = 1.6 kΩ and R2 = 320 Ω.

 

Closing Thoughts

The difference amplifier configuration is capable of interfacing the ADAQ798x with bipolar signals with many amplitude and frequency ranges, and it is remarkably simple to design. There are a couple other things to watch out for, though.

First, remember that some applications are concerned with achieving a high input impedance. With the other configurations we’ve discussed, this is possible by increasing the resistor values (and reducing the input bandwidth to take care of the additional noise). This configuration struggles to do this, however, as Rf and Rg can’t be too large that they impact ADC driver stability. The input impedance of this circuit is equal to that of an inverting amplifier:

 

To achieve an input impedance of 1 MΩ for example, Rg would need to be 1 MΩ, and Rf will likely be too large for the ADC driver to function correctly (at least when using common gains). The only practical way to increase the input impedance of the system would be to use another signal conditioning stage in front of the ADC driver.

 

The bright side is that since this configuration will likely feature smaller resistors, it is less likely that it’ll require extra filtering to compensate for resistor noise. Also, this makes balancing the offsets created by the input bias current more practical, as R1 and R2 can be easily selected to balance the offset from Rf and Rg. These two qualities allow this configuration to achieve higher levels of precision and higher signal bandwidths than the non-inverting configurations that could achieve higher input impedance.

 

Also worth mentioning is that this configuration can be used more easily in single-supply applications where the ADAQ798x’s negative supply is tied to ground. This is because the amplifier’s inputs are held at a constant dc voltage, and there’s less concern of violating the input common mode voltage specifications (shown in the ADAQ7980/ADAQ7988 data sheet).

 

Thanks again for joining me in this blog series! In our next and final entry, we’re going to look at an active filtering configuration for the ADAQ798x. Follow the EngineerZone Spotlight to be notified when the next addition to this series is available!

 

Have any questions? Feel free to ask in the comments section below!

Welcome back to the ADAQ798x ADC driver configuration blog series! In our last post, we looked at a modification to the non-inverting configuration for bipolar inputs within the range of ±VREF, but wasn’t compatible with signals larger than that. Today, we’ll look at a configuration that introduces another slight modification that enables the ADAQ798x to convert larger bipolar signals (±10 V, for example). We’ll first see how to select the relevant resistances to achieve a desired input range, and then look at how these values affect the system’s input impedance and noise floor.

 

The Non-inverting Summing Configuration for Attenuation

The following configuration can be used to perform bipolar-to-unipolar conversion with attenuation for signals larger than ±VREF.

 

This configuration is similar to the one we discussed last time, except Rf and Rg are no longer required and R3 is added to provide extra signal attenuation. The transfer function for this configuration is:

 

 

The math required to derive the ratios of R1 to R2 to R3 is a bit more complicated this time, but we can use a similar method as we did in the previous configuration. After finding the ratios of the resistors, one can select specific values depending on the needs of the application. In the interest of brevity, we won’t go through every step of the derivation, but we’ll see how transfer function simplifies to give us the resistor ratios when looking at the minimum and maximum values of vIN.

 

The ratio of R1 to R2 is found using the configuration’s transfer function by plugging in the minimum value of vIN, which results in vAMP_OUT equal to 0 V:

 

R3 drops out of the equation, and solving for R1 and R2 gives:

 

 

The ratio of R1 to R3 is found by plugging in the maximum value of vIN, which results in vAMP_OUT equal to VREF:

 

 

This time, R2 drops out and solving for R1 and R3 gives:

 

 

At this point, we can pick a value for any three of these resistors (given VREF and the range of vIN) and then calculate the value of the other two. As before, the major trade-offs are input impedance vs. system noise and offset error. The input impedance (ZIN) of this circuit is:

 

 

Let’s revisit the example we mentioned last time, where vIN = ±10 V and VREF = 5 V, and design the configuration with an input impedance of 1 MΩ. For this combination of vIN and VREF, R1 must be twice R2 and equal to R3. Using the ratios of R2 and R3 to R1 in the input impedance equation, we get R1 = 750 kΩ. R2 and R3 are therefore 375 kΩ and 750 kΩ, respectively.

 

As we mentioned in "Adding Gain for Bipolar Inputs", there is a trade-off between input impedance and system noise performance. Achieving a high input impedance requires large resistors, which produce more thermal noise and interact with the input current noise of the ADC driver to create more input voltage noise. Both of these increase the effective rms voltage noise at the input of the ADC, which can dramatically degrade performance. In the above example, the total system noise is roughly 334 μV rms (with a 5 V reference, dynamic range drops a whole 15.5 dB, from 92 dB to 74.5 dB)!

 

But there’s hope! This configuration can actually achieve near optimal performance if we limit its input bandwidth. For instance, if we limit the input bandwidth in the above example to 20 kHz, the full system noise drops by almost a factor of ten to 48 μV rms (a dynamic range of 91.4 dB for VREF = 5 V)! We can limit the input bandwidth (BWin) by adding a shunt capacitor CS, as shown below. Note that for these noise calculations, we can treat R1, R2, and R3 as a single resistor, RS, where RS is the parallel combination of R1, R2, and R3.

 

 

 

MT-049 shows how to calculate the noise created by RS (including thermal noise and its interaction with the ADC driver’s input current). The main difference for the ADAQ798x is that the noise bandwidth is set by the integrated RC filter (rather than the amplifier bandwidth, as it is in the tutorial). The rms noise that RS adds at the input to the ADC is:

(eis the Johnson noise of Rand G is the ADC driver gain.)

 

CS reduces the noise that reaches the ADC by reducing the bandwidth at the ADC driver’s input. If the cutoff frequency of RS and CS is much smaller than that of the integrated RC filter’s (4.42 MHz), then the noise contributions from RS can be calculated using RS and CS in place of R and C in the above equation.

 

The total system noise is the root-sum-square of the individual noise sources in the ADAQ798x, including those from RS, the ADC driver’s input voltage noise, and the ADC’s RMS noise. The following plot shows the system noise vs. the input bandwidth for several values of RS.

 

 

Note that as input bandwidth decreases, the full system noise tends towards the ADAQ798x’s total rms noise (44.4 μV rms). This means that noise benefits of reducing the bandwidth give diminishing returns at a certain frequency, which depends on the effective value of RS.

 

Closing Thoughts:

In today’s post, we looked at an ADC driver configuration that allows the ADAQ798x to accept bipolar inputs that are larger than ±VREF, and how to calculate the input impedance and system noise based on the resistor values (and with an optional shunt capacitor CS).

 

Although adding CS proved to reduce noise, it also limits the usable input bandwidth. For this reason, it’s often impractical to achieve a high input impedance when using this configuration for wide bandwidth applications. This configuration is typically only recommended for low bandwidth applications that require high input impedance. (If your application requires wide bandwidth, be sure to tune in to our next post!)

 

Thanks again for joining me in this blog series! In our next entry, we will be stepping away from the non-inverting configurations we’ve discussed to this point to look at a difference amplifier configuration for bipolar inputs. Follow the EngineerZone Spotlight to be notified when the next addition to this series is available!

 

Have any questions? Feel free to ask in the comments section below!

Welcome back to the ADAQ798x ADC driver configuration blog series! In today’s entry, we’ll look at one of the configurations that can be used to interface the ADAQ798x to bipolar sensors and input sources. These types of signals are common in industrial and data acquisition applications. This configuration builds on the non-inverting configuration we discussed previously to convert a bipolar signal to a unipolar one for the integrated ADC.

 

The Non-inverting Summing Configuration

Bipolar signals swing above and below ground (0 V). Since the ADAQ798x’s integrated ADC can only convert signals between 0 V and VREF, bipolar signals need to be dc-biased and properly scaled for the ADC. The following configuration accomplishes this by adding two resistors (R1 and R2) to the standard non-inverting configuration.

 

 

This configuration performs bipolar to unipolar conversion by summing the input signal with a separate dc voltage to bias the ADC driver’s output to the ADC’s midscale input (VREF/2). Using the reference source (VREF) as the dc voltage is often practical, as it eliminates the need for additional circuitry (the ADAQ798x is always accompanied by a reference source anyway!). It also prevents deviations in VREF from adding offset error to the system, since the ADC driver’s dc bias will always be half of VREF. For these reasons, we will look specifically at this configuration utilizing VREF as the dc “shifting” voltage.

 

The transfer function for this configuration is:

 

 

Similarly to the regular non-inverting configuration, the ratio Rf and Rg determines the gain from IN+ to AMP_OUT, but this ratio now depends on the input amplitude of vIN as well. Note that vIN is bipolar, but the voltage on the non-inverting node is unipolar. That means that for the minimum value of vIN, the voltage on IN+ must be 0 V:

 

 

This relationship gives the ratio of R1 to R2:

 

 

Rf and Rg can be determined using the configuration’s transfer function and the condition that the output of the ADC driver (vAMP_OUT) is equal to VREF/2 when vIN is 0 V. Solving this equation for Rf and Rg gives:

 

 

We now have the ratios of R1 to R2 and Rf to Rg, but we still need to pick specific values. We addressed selecting Rf and Rg values in our previous post. R1 and R2 selection should be determined based on the application’s noise, accuracy, and input impedance requirements. Small resistances will improve noise and can reduce offset errors caused by its interaction with the ADC driver’s input bias current (see MT-038 and CN-0393), but large resistances are required to increase input impedance and reduce the output current of the reference source. The input impedance of this circuit is:

 

 

Note that for the specific case where the amplitude of vIN is ±VREF, the ratio of Rf to Rg is 0. In this case, the ADC driver gain is 1, meaning Rg is omitted and Rf can be 0 Ω.

 

Let’s look at an example where the ADAQ7980 needs to perform bipolar-to-unipolar conversion of a ±1 V input signal, with VREF = 5 V and using Rf = 2 kΩ. Using the above equations, R2 must be 5 times R1 and Rf must be 2 times Rg. Since Rf is 2 kΩ, Rg must be 1 kΩ. Specific values of R1 and R2 can be selected depending on the application’s requirements. For this example, we’ll aim to select a combination of R1 and R2 that negates the effects of the input bias current on offset error. MT-038 explains that R1||R2 should be equal to Rf||Rg to achieve this, which gives R1 = 800 Ω and R2 = 4 kΩ.

 

But let’s also consider an example where vIN = ±10 V with VREF = 5 V. In this case, we run into a problem where the ratio of Rf and Rg is a negative number, so we can’t actually achieve this input range with this configuration. In fact, the largest vIN that will work with this configuration is ±VREF, where the ADC driver gain is equal to 1. Luckily, we’ll be looking at two other configurations that will allow us to extend past this input range in future entries to this series!

 

Closing Thoughts

The above configuration can also be used for unipolar signals by connecting R2 to ground instead of VREF. This modification is useful for unipolar input signals that need to be attenuated for the ADC (with amplitude >VREF). In this case, the ADC driver will most likely be in unity gain, so Rf and Rg are not necessary.

 

As mentioned above, if the application requires a high input impedance, R1 and R2 must be large, which can increase the noise floor of the system. We can compensate for the increased noise with the addition of a shunt capacitor and/or by oversampling and decimating. Both of these options sacrifice input signal bandwidth to reduce the noise floor. For low-bandwidth or dc applications, however, the input bandwidth is not as important. For this reason, these configurations are better suited for low-bandwidth, high input impedance applications. We'll discuss this in more detail in our next post.

 

One problem this does not address, however, is the offset error caused by the ADC driver’s input bias current flowing across the resistors. Large resistances result in large dc errors. This error can be reduced at the expense of some input range by adjusting the ratio of R1 and R2 to compensate for the undesired voltage drop, or by selecting Rf and Rg values that cancel out the offset caused by R1 and R2. However, keep in mind that Rf must be small enough to ensure amplifier stability, so the second option is not always viable.

 

Thanks again for joining me in this blog series! Next week, we’ll look at another modification to the non-inverting configuration designed for use with bipolar input signals that are too large for the one we discussed today. Follow the EngineerZone Spotlight to be notified when the next addition to this series is available!

 

Have any questions? Feel free to ask in the comments section below!

One hundred and eighty decisions. That’s what the average driver makes every minute. That’s three decisions every second. Pretty impressive.

 

Now imagine you’re driving and an oncoming vehicle drifts into your lane. At the same time, a man is walking his dog on the sidewalk. Do you brake hoping to minimize a collision? Do you swerve and possibly hit the man and dog? Do you veer the other way and into oncoming traffic? Whichever you choose, it’s unlikely you escape without causing substantial harm or damage.

 

Even with the ability to make three decisions in a split second, this still becomes a no-win situation for just about all of us. Unless we can avoid it.

 

And that’s one of the main goals of autonomous driving: vehicles where sensing, communication, actuation, and artificial intelligence (AI) work together to gather information, analyze it, and make decisions faster and sooner than even the best human drivers.

 

Extending awareness beyond what the eye can see.

 

Most of the information we get while driving comes from what we see. And that can be limited and impacted by many variables, such as weather, distance, and distraction. As a result, a lot of the decisions we make behind the wheel are reactive. Autonomous vehicles offer the promise of predictive driving. To realize that, they need sensing capabilities far greater than ours.

 

Three technologies that are central to sensing the external environment of autonomous vehicles are RADAR, LiDAR, and High Performance IMUs.

 

                     

 

RADAR

RADAR is currently being used extensively in Advanced Driver Assistance Systems (ADAS) applications such as collision warning and mitigation, blind spot monitoring, and lane change assistance. Approximately 50% of all recently produced RADAR modules contain technology from Analog Devices (ADI). With a 15-year track record in automotive RADAR, Analog Devices is now developing the innovative Drive360™ RADAR technology platform to deliver the highest level of performance and distance resolution available. The Drive360™ RADAR platform is engineered to support the full 76-GHz to 81-GHz frequency band and provide for platform longevity.

 

Drive360™ RADAR is built around 28nm CMOS technology, so it can provide the highest degree of digital signal processing integration flexibility, while at the same time, ADI’s RF IP allows for highly differentiated waveform and algorithm implementations. Drive360 RADAR-enabled products will reliably detect smaller, faster moving objects at longer distances (e.g. motorcycles, pedestrians, animals), providing the critical time to avoid injuries or fatalities.

 

LIDAR

While RADAR is central in the future of all-weather autonomous driving, it is only part of the solution for split-second decision making. Other sensors are needed, such as cameras and LIDAR. LIDAR (Light Detection and Ranging), with its range and accuracy, will be key in solving the most difficult ADAS challenges, and is an area of rapid and intense development. ADI is currently focused on solid-state LIDAR designs based on the same material found in computer monitors to scan light. The cost-effective design overcomes the current prohibitive cost and improves reliability by eliminating the moving parts found in conventional offerings. It also offers improvement in key performance metrics such as range, resolution, frame rate and power consumption.

 

Inertial Measurement Units

Along with seeing the surrounding environment, autonomous vehicles also need to feel and respond to the road in all weather conditions. Analog Devices Inertial Measurements Units (IMUs) combine multi-axis accelerometers and gyroscopes with processing and calibration in a single package. IMUs, in conjunction with on-board ADAS and satellite localization inputs, provide an accurate picture of a vehicle’s position and heading, while rejecting shocks and vibrations from normal driving.

 

Looking beyond

Much as RADAR, LIDAR and High Performance IMUs can extend the sensing capabilities of autonomous vehicles beyond what we can see; Analog Devices is looking beyond how those technologies are used today. Think about this. In the 1990s, when a new cell phone came out, it may have had a better battery or a thinner profile. Then came the smartphone, which fundamentally and permanently transformed and improved how we live. That’s how we’re looking at the technology we’re about to introduce for RADAR, LIDAR, and High Performance IMUs. These transformational technologies will be the foundation of advanced safety and autonomous driving applications to come.

  

To learn more about Analog Devices and the future autonomous driving, please visit analog.com/adas. Have some thoughts about autonomous driving and safety? Please share your comments below.

 

Welcome back to the ADAQ798x ADC driver configuration blog series! As we discussed last time, we will be looking at several common and useful configuration options for the ADAQ798x’s integrated ADC driver, how to design them, and what to watch out for when doing so. In today's entry, we will discuss how to use the common non-inverting configuration to interface the ADAQ798x with unipolar input sources that are smaller than the ADC’s input range of 0 V to VREF.

 

The Non-inverting Configuration

 

Recall from our last post that the ADC converts inputs between 0 V and VREF. This means the ADC driver’s output must also be 0 V to VREF to allow the system to utilize the full range of 216 codes that the ADAQ798x provides. The ADAQ798x’s integrated ADC driver can provide gain to give the necessary boost to signals with smaller amplitudes.

 

That’s where the non-inverting configuration comes in! This configuration provides gain for unipolar signals, a high input impedance, and requires only two additional resistors.

 

 

Many system designers already know how the non-inverting configuration works, but we’ll look at it here in the context of the ADAQ798x, and how the configuration can impact key system performance parameters, including system noise, signal-to-noise ratio (SNR) and total harmonic distortion (THD).

 

First, how do we select the resistors Rf and Rg given the application’s input range and reference voltage? The voltage at the output of the ADC driver (vAMP_OUT) is:

 

 

Given that vAMP_OUT is between 0 V and VREF, the ratio of Rf to Rg is easy to calculate for the application’s input range (vIN+):

 

 

After calculating the Rf to Rg ratio, their specific values must be selected. The “right” value of these resistors depends on the application, and must be chosen to balance system noise performance with power dissipation, distortion, and amplifier stability. Lower values of Rf result in lower noise, but also results in more current drawn from the ADC driver’s output (increasing power consumption). Using higher values of Rf can limit this power consumption, but results in added system noise and potential stability issues.

 

The amount of noise a resistor generates is proportional to its resistance. Large resistors contribute more noise, and can impact the system’s noise floor and ac performance specs (like SNR). The total system noise can be calculated by taking the root sum square of the individual noise sources in the circuit, including the resistors, the ADC driver, and the ADC itself:

 

 

where vn,system is the system rms noise floor, vn,ADC driver is total noise of the ADC driver circuit (including the external resistors), and vn,ADC is the ADC’s noise floor specification.

 

The ADAQ7980/ADAQ7988 data sheet explains how to calculate the vn,ADC driver (see Noise Considerations and Signal Settling), and gives vn,ADC as 44.4 μVRMS for a 5 V reference. CN-0393 also explains how to calculate the expected SNR of the system based on the total system noise (see System Noise Analysis). In the interest of brevity, we won’t do those calculations again here, but we’ll give another example here.

 

Let’s look at a case where the ADAQ7980 needs to interface directly with a sensor with an output range from 0 V to 2.5 V while using a 5 V reference. Since the sensor’s output amplitude is equal to half the ADC’s input range, the ADC driver should be set in a gain of 2. This requires Rf to be equal to Rg, but the selection of Rf is somewhat flexible. First, let’s look at how different values of Rf (and Rg) affect the system’s noise floor and corresponding expected SNR:

 

Rf, Rg (Ω)

vn,system (μVRMS)

Calculated SNR (dBFS)

500

53.3

90.4

1k

54.3

90.2

2k

56.4

89.9

5k

62.2

89.0

 

As you can see, the system noise will increase and SNR will degrade when using higher values of Rf and Rg. Increasing gain will also degrade SNR performance because it increases the effective noise contributed by the ADC driver’s input voltage noise and Rg. The plots below show measured results for SNR and THD (total harmonic distortion) for various gains using Rf = 1 kΩ (input frequency = 10 kHz).

 

 

 

One of the down sides of selecting smaller resistors, however, is that the ADC driver needs to deliver more current (and therefore power) through the feedback network. The instantaneous current going through Rf and Rg is vAMP_OUT divided by the sum of Rf and Rg. This current adds to the total power dissipation of the system, and should be limited in low-power applications.

 

Closing Thoughts

One benefit of this configuration is that its input impedance is very large, since the source is tied directly to the non-inverting node of the ADC driver. This is especially useful for sources with significantly large output impedance. We’ll see that this is not always the case for other configurations.

 

Although the non-inverting configuration can provide gain, there are some practical limitations. First, as stated in this Analog Dialogue article, the ADC driver must maintain a certain large- and small-signal bandwidth to achieve forward- and reverse-settling requirements of the ADC. Bandwidth is inversely proportional to closed-loop gain. The system noise also increases with gain, and at a certain point will degrade performance too much to be viable without considerable filtering (which we’ll cover later in this series).

 

Also, for applications that require very low offset and gain error and drift, be sure to use precision resistors with adequate tolerance and TCR specifications. If possible, use matched resistor networks that specify the tracking of resistance and TCR between their individual resistors (for example, the LT5400 series). CN-0393 explores this concept in greater detail. Note also that the ADC driver’s input bias current will flow through Rf and Rg, which will create voltage offsets in the system. A resistor can be placed between the non-inverting node of the ADC driver and the input source to balance out these offsets, but remember that this resistor will also add noise to the system! (See MT-038 for more information.)

 

Thanks for reading! Next week we’ll look at two of the ADC driver configurations designed to allow the ADAQ798x to interface with bipolar input signals. Follow the EngineerZone Spotlight to be notified when the next addition to this series is available!

 

Have any questions? Feel free to ask in the comments section below!

In the unrelenting march towards high channel density, many system designers are searching for data acquisition solutions that use less board area, while still meeting strict performance criteria. ADI is meeting these challenges head-on with its first family of μModule® Data Acquisition Systems, the ADAQ7980 and ADAQ7988. The ADAQ798x family integrates common signal processing and conditioning blocks into a system-in-package (SiP) design that enables high channel density, simplifies the design process, and provides exceptional performance.

 

The integration of the ADC driver, critical passive components and the SAR ADC into a single package simplifies the design process, reduces component count and enables increased channel density while guaranteeing signal chain performance. The ADC driver configuration is flexible as well, and enables the ADAQ798x to directly interface with sensors and input sources with varying input voltage and frequency ranges. This flexibility makes the ADAQ798x suitable for a variety of industrial, instrumentation, communications, and healthcare applications.

 

The goal of this blog series will be to help system designers take full advantage of the ADAQ798x family’s flexible front-end, and show how it can be configured to fit their application. We will be examining common and useful ADC driver configurations, how to implement them using external passive components and some of the “gotchas” to look out for in each configuration.

 

Why Configure the ADC Driver Anyway?

The ADC driver is used to condition the input signal and acts as a low-impedance buffer between the signal source and the SAR ADC’s switched capacitive input. The ADAQ798x takes a “best of both worlds” approach with its ADC driver by providing the benefits of signal chain integration while still providing design flexibility that supports a variety of applications. The integration of the ADC driver into the ADAQ798x reduces board area and eliminates the (sometimes daunting) task of selecting an appropriate amplifier (as explained here). The ADC driver's configuration is still flexible, however, because its inputs and output are routed directly to the pins on the device, allowing for the addition of external passive components to implement gain, filtering, etc. This enables the ADAQ798x to support signal amplitudes and bandwidths present in many precision applications.

 

 

 

We'll be looking at several common ADC driver configuration options for the ADAQ798x in future posts. Before we get into the specifics of those configurations, though, let's establish some of the common design considerations for the ADC driver for many applications. First, we’ll start with input voltage range:

 

The ADAQ798x’s integrated ADC converts unipolar, single-ended signals from 0 V to VREF to a 16-bit result. VREF is the reference voltage, which is generated externally and can be set from 2.4 V to 5.1 V. The ADC driver must be configured to translate the input source’s output range to fit the integrated ADC’s input range.

 

 

 

The ADAQ7980/ADAQ7988 data sheet specifies performance with the ADC driver in a unity gain configuration, where the voltage input at the IN+ pin is 0 V to VREF. This configuration is the simplest design (it only requires shorting the IN- and AMP_OUT pins together!) and achieves the best noise performance and power consumption, but isn’t always practical, as many sensors and sources don’t adhere to the ADC’s input range. Industrial applications, for example, frequently involve bipolar signals with amplitudes as large as 20 VPP!  Luckily, with the addition of a few passive components, we can implement gain, attenuation, bipolar-to-unipolar conversion, and active filtering, potentially eliminating the need for more amplifiers in the signal chain.

 

As we delve into some configuration options in future posts, we need to keep some key design considerations in the back of our minds. Examples of these include:

 

  • Power Consumption
  • System noise
  • Large- and small-signal bandwidth
  • Input impedance
  • Settling characteristics
  • Distortion
  • Offset error
  • Gain error

 

The requirements for each will differ for each application, but all of them are impacted by the ADC driver configuration, and the components used. For example, using large-valued resistors will typically reduce power consumption and increase input impedance, but can increase system noise, distortion, and offset and gain errors. We’ll examine each of these parameters as they pertain to specific configurations in future blog posts.

 

Thanks for reading, and I look forward to engaging with you in future blog posts. Follow the EngineerZone Spotlight to be notified when each addition to this series is available.

 

Have any questions? Feel free to ask in the comments section!

DDS Advancements

Posted by JLKeip Jul 25, 2017

My previous entries discussed the when/where/why to use a DDS based approach in place of a PLL-based one. One reason folks lean towards PLLs though, is familiarity.  So let’s get more familiar with DDS. (Note this does not require reaching out to the American Dental Association)

Before I launch into a brief review of a state-of-the-art DDS, let me once again direct you to the tutorial we put together here if in an effort to avoid rehashing something that’s already published. 

If you have any questions after running through the tutorial, please ask them, then come back and keep reading...

So let us jump ahead to exploring the elements of a more advanced DDS architecture with more powerful capabilities.

The blocks in white were explained in the tutorial, they are fundamental necessities for any DDS system. I’m going to speak on the green blocks.  They are critical to many of the advantages I wrote about before.  A valuable addition not depicted above is the existence of alternate FTW registers which can be switched between for frequency hopping/FSK applications.

The Phase Offset Register (POR)

   On its own, the addition of this block provides some valuable functions.

  1.  The ability to digitally reposition the phase of the output in very small steps (as small as 0.0055 degrees in some of our products). The digital nature of the function makes it both perfectly repeatable and reliable.
  2. The ability to implement Phase Shift Keying (PSK) by toggling between multiple Phase Offset Word (POW) options.
  3. The ability to establish perfect quadrature for two DDS channels relying on a common system clock source AND the ability to compensate for static phase offsets which might degrade the perfect quadrature.

The Amplitude Scale Factor (ASF)

   Provides a similar level of control to Amplitude as the POR provides to the phase of the signal.

The Auxiliary Accumulator

   By far the most powerful of these additions, though some of that power results from combining it with the POR or ASF capability.  To date, these are the most meaningful uses for that auxiliary accumulator

  1. Originally this was introduced to enable frequency sweeps. Using a second accumulator enables the establishment of a steadily increasing variable. Advanced DDS's may be configured to add that variable to the base Frequency Tuning Word. The net result is a signal with a steadily changing frequency.
    • Alternatively, you can combine it with the POW to create a phase sweep.
    • Alternatively, you can combine it with the ASF to create an amplitude sweep.
  2. Programmable Modulus – I wrote about this already in prior Blogs, and you can read more about it here.
  3. Phase coherent switching. By its nature, a DDS switches phase in a phase continuous manner. Some applications (radar and others) want switch phase coherently instead.

 

Phase coherent switching is a topic with enough depth that it warrants separate treatment, if you want to know more, before I get back to blogging again, respond to this email or post a question to the DDS forum.  As always, any questions raised in the minds of readers will gladly be fielded as well.

tnelson654

EcoAnalog: Solar

Posted by tnelson654 Employee Jul 19, 2017
Evolution left my Scandinavian ancestors with a low level of photoprotective pigment, melanin. That, and a receding hairline, gave me an appreciation for the power of the sun here in California. UV radiation can cause a sunburn in as little as 15 minutes - trust me. Did you know that lower frequencies of sunlight (less than the sunburn) are sufficient to make electrons jump the band gap in solar cells within a photovoltaic (PV) system? Stack enough solar cells in a big enough array and you can generate some serious power without the negative side-effects of fossil fuels.

 

Solar cells are made of semiconductor material. It's not exactly the same as the semiconductor materials made here at Analog Devices. We make a range of devices that help efficiently convert the power from those solar cells into stored charge in batteries, or electricity for the grid.

 

Since you are already in EngineerZone, search for "solar" and you'll find a post on hacking an LED solar garden light. I like that one because those little panels are smaller than my forehead. Instinctively, I know that the top of my head burns more quickly than the front of my head - being perpendicular to the sun is more efficient! Many solar panels have motors which change the angle throughout the day but even static panels can be optimized. And that leads us to the concept of maximum power point tracking (MPPT). The load characteristics can be optimized for maximum transfer efficiency from the solar cell. This is done with a control loop that monitors the current and voltage (the I-V curve) and keeps it at the point of maximum power. ADI makes DC-DC converters that work with MPPT as well as microcontrollers to do the math. We also make converters to efficiently store all that charge at the right voltage for different types of batteries - a DC application. DC applications have a global impact but not as much as AC - meaning, household electricity. To make AC electricity out of a solar panel requires an inverter.
Solar portfolio
For a traditional inverter, ADI makes ARM-based mixed-signal control processors, Sigma-Delta-based current sensors, and iCoupler products to interface signals from dangerously high voltages. Recently, we partnered with SolarEdge to launch what may be a revolution based on the industry’s first system-on-a-chip for solar inverters. They are the fastest ARM Cortex® M4 based control processors of their kind, and feature the industry’s most precise A/D converters, 16-bit accuracy, and a series of dedicated hardware accelerators. Continuing from the announcement, "The ultimate result of SolarEdge and Analog Devices’ collaboration is an inverter that’s half the size and weight of traditional inverters with a significant reduction in all that expensive metal." (That metal being all the bulky magnetics used to filter the AC waveform for processing in traditional inverters.) Trust me, that's cool!

 

Hopefully, this continues the trend away from fossil fuels as a source of electricity around the world. Somewhere in Scandinavia, distant relatives of mine are converting their countries to solar power. Oh sure, their panels are nearly vertical half the year but it is still remarkably efficient. I read somewhere that parts of Norway are better for solar power than parts of Germany due to cloud cover. I've got distant relatives there, too. The point is, wherever there is sunlight, there can be power, unlike sunburns which are more dependent on latitude and less on cloud cover. Again, trust me on that. It's unlikely that evolution will prevent future generations from getting sunburns as easily, but with the help of ADI it's very likely that those generations will get most of their power from solar.

 

To keep up on the latest technology and advancements from ADI be sure to follow The EngineerZone Spotlight.

In a recent blog, I highlighted three ways high-speed converter innovations are changing our world.

 

High-speed converters are playing roles in other areas as well. As more and more devices move into the digital domain, our newest converters are simplifying the RF signal chain. Let’s take a journey into the evolution of the humble car radio as an illustration of this change.

 

 

In the classic version of the FM radio in your car, frequency translation, filtering, and amplification were done by analog RF blocks to tune into the station of choice, eliminating all other radio stations except the station you wanted before going to the analog-to-digital converter (ADC).

 

 

 

 

In the digital radio, we push the ADC up close to the antenna. The station select functionality is pushed into the digital domain – the converter is now required to capture the entire FM radio band (100× the bandwidth).

 

Better for automobile owners.

For you and your friends in the car, the potential functionality of the radio is greatly increased. If some additional digital circuits are added, we can make it possible to tune into multiple radio stations simultaneously (e.g. get weather or traffic info in “sidebands".) Or, by making the digital circuits programmable, you could access a software download that would allow your radio to adapt to new standards, such as HD radio.

 

Better for automobile manufacturers.

For Analog Devices customers, the RF signal chain is greatly simplified, making hardware design easier and allowing for a radio that would be capable of providing new services in the future.

 

That’s just one example of how this kind of RF signal simplification can provide real benefits for manufacturers and consumers. This level of simplification, enabled by the unprecedented bandwidth of Analog Devices high-speed converters can extend to many more applications such as cable, 5G, and radar; to deliver new services and capabilities.

 

Keep this in mind when you cue up that favorite playlist on your next road trip.

You can call me biased but when I hear the term “IoT platforms” and the exclusivity of conversations around software offerings under this term, I cringe. Does this mean hardware is a given?

 

Yes I’ve woken up and smelled the coffee, I get the concept of IoT where it’s the data that counts and the infinite possibilities of what you can do with this data to improve uptime efficiency of e.g. cities, factories or our bodies (boy do I need that one…)

 

So how important is the hardware? Well in IoT applications that require very rudimentary sensing, such as a coarse measurement of whether somebody is in the room, or if a garbage container is full; you could argue that hardware is less relevant. Cloud computing and its availability to the masses means that insight can be gained from a huge number of these rudimentary sensor readings and value can be gained from these insights.Tying these sensor readings with time, location and other sensors is a big part of what is envisioned in the explosion of sensors in the coming years and the ability to monetize with software and services. I get it.

 

There’s a caveat however. I believe that many of the most challenging IoT applications can only be realized with hardware that actually does not exist today.There are still significant challenges in hardware design, and will be for a long time. It’s fairly logical but let me share some reasons why. 

 

Firstly, what’s the advantage of IoT? Well it’s the remote monitoring and control. This goes for product solutions as well. It makes sense to “future proof” your hardware making it a scale better than you need. After all what’s easier? Running around swapping out units to provide the next big feature that your product requires or a simple software OTA (and isn’t that the whole point of IoT?)

 

Secondly, I’ve said it before and I’ll say it again: Send garbage up to the clouds and that is what you'll get back - don’t expect insight out of the cloud. There is a base line of how good your data needs to be to make your analytics work effectively. Good data requires careful hardware.

 

And lastly, and yes I am biased. (well I am the semiconductor dude don’t you know). But actually, this is because I’m also passionate about the piece we play in this. For Analog Devices, IoT is simply extending a signal chain up to the cloud. But we have seen how signal chains have developed over more than 50 years (and personally I’ve been on that journey for 30 of them – back to the uptime efficiency of bodies...). No one has ever asked me could they do less, they have always asked me what can be measured faster, easier, at lower power, at higher WHATEVER to give their customers something better than anyone else. Trust me. Pay attention to your hardware.

 

From Rant to Reality

And if you’d like to check out one of ADI’s IoT Arduino based Wireless Development Platform for Internet of Things applications feel free.

www.analog.com/IoT

Full disclosure: I’m in the high-speed converter business helping Analog Devices to write the next chapters on Signal Conversion.

 

A new generation of high speed converters engineered to exploit deep sub-micron CMOS technology and proprietary architectures is poised to provide industry-leading performance in critical high-dynamic range parameters.

 

Here are three ways they’ll be driving the next wave of gigahertz bandwidth, software-defined systems.

 

1. Making 5G Come Alive

From car-to-car communications to watching the big game on your handheld device, you’ll want the speed and immediate response time promised by the 5G network. A key to putting the power of 5G into the hands of people everywhere will be using high-speed 28nm analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) in both base station applications and 5G connectivity equipment.

 

Analog Devices has a long history of developing high speed converters—including the new 12-Gsps 16-bit RF DAC, and 3-Gsps 14-bit RF ADC.

 

  1. Keeping Us Safe

With growing unrest in hotspots around the globe, threat monitoring and detection are more important than ever. All-digital phased array radars are dramatically enhancing our capability to identify and mitigate risks to our security. High-speed RF ADCs from Analog Devices are pushing the state-of-the art in analog input bandwidth and dynamic range for pipeline converters, while also delivering significant reductions in size, weight, and power (SWaP). These innovative products are enabling all-digital phased array radars to be added to existing aircraft and ships, making our world safer.

 

  1. Delivering a Better Television Experience

Game of Thrones. Stranger Things. Atlanta. There’s a lot of great TV out there to enjoy. And to do that, people are demanding that their broadband and wireless service operations provide higher quality, always-on data, and video streaming. The AD9162 DAC delivers unprecedented bandwidth and dynamic range, enabling ultra-high-definition (UHD) and 4K television across more channels at extraordinary streaming and download speeds— bringing the future of TV into homes for all of us to enjoy.

 

There’s a lot more to look forward to when it comes to high-speed converters. Follow the EngineerZone Spotlight for more blog posts in the future.

The amount of data that goes up to the cloud is creating a data lake that continues to get bigger and bigger. I’m not adverse to this but what’s the point? How much data do you need or use? Which neatly brings me onto useless IoT applications. (I had to find a reason to get onto this…)

 

I recently read a review of a wireless kettle (and believe me before I instigate a wave of responses – “annoyed from Andover” anyone? I’m sure there are many perfect wireless kettles out there but honestly why can’t you just get off your chair and flip the switch?)…there are some applications that just don’t make sense to me.

 

 

I’ll share some feedback on getting this kettle going “On the first attempt to boil the kettle, it was hampered by a forced-debugging, which consequently caused its base station to reset. The kettle's base station appeared unable to connect with the kettle itself. On re-calibration it seems that it had never connected at all. Then the flaky WiFi connectivity required constant checking and resetting.” – all for a cup of hot water anyone? Now if that kettle had been monitoring the level of water and looping back into my system to pass up a local value of how much power it used to boil back into my home efficiency system then I get it. This is what I call sending insight versus just data.

 

Smart partitioning and embedding of algorithms in the sensors allows the most critical data to be interpreted in real time at its source. Algorithms embedded into intelligent sensors and in the cloud allow for interpretation beyond what can be done with silicon alone. In fact, this leads to the possibility for prediction and anticipation of future system behavior. Accelerating adoption of IoT solutions in mission critical applications is dependent on the ability to build secure systems – intelligent partitioning enables this.

 

In Industrial automation, active machine monitoring can transform factories by radically improving uptime efficiency. For example, accurate MEMS vibration sensors and algorithms to predict machine performance and reliability both locally for real-time optimization and intervention and in the cloud where information from multiple systems across multiple factories can be aggregated, analyzed and acted upon to improve productivity.

 

In mobile healthcare, clinical grade monitoring of vital signs and other wellness indicators are becoming increasingly important. For example, energy efficient optical sensors and ultra-low power MEMS sensors that reliably assess patient health in real-time using advanced algorithms. Another example is motion rejection algorithms which more accurately interpret heart rate from optical measurements at a patient’s wrist. Resultant information can then be transferred to the cloud for remote processing that may include more intensive data analysis used to derive further bio-markers and trends.

 

And that’s why you should use the cloud effectively (and why you should not have to wait hours for your kettle to boil…)

 

From Rant to Reality:

For intelligence at the edge you need low power processing. More information on the latest ultra low power MCU that enables 10 times system-level power savings

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