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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 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.



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 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)














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.


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.

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.


For more Inside IoT blogs click here.

In short because we’re at the battlefront of every IoT application. We hardened semiconductor warriors are on the front line taking any and every kind of analog signal and transferring it to the digital world.


Does light, pressure, vibration temperature movement come in a nice neat digital format? You bet not. So Analog Devices (ADI) is one of the translators of real information into usable data. We focus on very difficult modalities because that’s our history, but simply said the first stage of the IoT battle is to sense (we do that too!), measure and then then transform into data. And frankly, how well you do this sets the stage for success further down the line. If you put garbage in then, sorry to disappoint you, but you’re going to get garbage out (big secret unveiled!). Need a location that’s within a mm, or a temperature that ensures you understand different growing conditions for unique crops, or a vibration that tells you exactly what ball bearing will fail next in your rotor? You need precision, and reliability. Don’t like changing batteries? You need measurement that is always on but also consuming ultra-low power (or self powered) to ensure even the most demanding IoT applications are empowered with the right data at the right time.


(If you really like changing batteries or love just writing analytics code for the sake of it don’t read any further and sorry to have wasted your time….)


So that’s why ADI is talking to you about IoT…. We’re one of the most important army commando units at the front line of the IoT battle enabling customers to create secure, reliable and robust IoT systems.


And silicon innovations enable interpretation of the world around us by bridging the physical and digital domains with technologies that sense, measure, interpret and connect.


From Rant to Reality

If you’d like to check out the latest low noise, high performance IoT MEMS sensor for Condition Based Monitoring Applications, click the MEMS sensor link for information.


EcoAnalog: Hybrid Vehicles

Posted by tnelson654 Employee Jun 15, 2017

On my bike ride downtown for coffee this morning, I waited in a double left turn lane surrounded by cars. I used to dread those moments because of the exhaust fumes. It's much better now thanks to hybrid vehicles and the new start/stop feature in traditional vehicles. Hybrid vehicles and stop/start gasoline-powered vehicles eliminate tons of carbon from polluting the atmosphere. Literally, tons!

According to the International Energy Agency in 2016, the transportation sector was responsible for approximately 23% of the world’s total carbon dioxide (CO2) emissions from fossil fuel consumption. On the occasion of their 9 millionth hybrid vehicle in April 2016, Toyota calculated that their hybrid vehicles produced approximately 67 million less tons of CO2 compared to conventional vehicles of similar size and driving performance. Today, almost every major automaker offers hybrid vehicles. So, certainly, that number is many times larger for the industry as a whole.


12V Stop Start

My first hybrid had the stop/start feature; it automatically shut off the engine at the first stop light and then started it back up when I wanted to go. It took some getting used to! And now that system has been applied to regular (non-hybrid) vehicles. For city driving, shutting off the engine at stop lights saves fuel and therefore CO2 emissions compared to idling at the stop light. Estimates range but many suggest 10% better fuel economy compared to the same model without stop/start. This has the potential to reduce much more CO2 than hybrid vehicles because there are so many more traditional vehicles on the road. I'm not going to attempt the math, but it's probably on the order of hundreds of millions of tons. Between the two technologies, that's a huge impact.


The algorithms differ by automaker. While traditional vehicles utilize a second lead-acid battery for restarting the engine, battery chemistry advances allowed hybrid vehicles to move from nickel-metal hydride to lithium-ion battery stacks. Accurately measuring and controlling the batteries is one of the keys to maximizing the performance of these systems. The stop/start system needs to be sure the battery is capable of restarting the engine before it stops it. The hybrid battery stack needs to keep each cell in the stack balanced with all the others, sharing equally and remaining fully charged. Isolating and level-shifting precision measurements of tiny voltages on a stack of hundreds of volts in an abusive environment is a challenge. It has to be robust, reliable and stable across temperature variations for the life of the battery system. And more systems use ADI than any other. Each generation is more integrated than the previous one, more precise, more reliable; and as they become more complex, the firmware support makes it easier to implement.


Schematic for battery monitoring


It's just what we do. So when I'm waiting at a red light on my bike, I don't have to breath all that exhaust, the planet doesn't have to absorb all that CO2 and I'm happy about that.

Like nearly everyone around me here, I am an engineer. And like you, since you are reading this "green" post, the future of the environment is important to me. I recycle, I commute via bike, and I have solar panels on my home. Many times in my career, I wondered if I should be working or volunteering somewhere that is directly involved with the cause. Should I be inventing a car that doesn't pollute, should I be building solar energy farms, should I be doing something more meaningful? Should I be somewhere else? And then I realized that I am in exactly the right place. I am doing extremely impactful and meaningful things with my engineering skills to help the environment! I just didn't realize it.

In future posts, my like-minded colleagues and I will link some recognizable (and a few obscure) "green" movements with the innovative technologies being developed here at Analog Devices. It's really quite inspiring! I believe we are really making a difference in the world while making the best use of our skills as engineers.

Chances are, if there's an emerging technology that might really help the planet, then we are already working on it. What green initiatives are you passionate about? Want to know if we are involved a particular environmental effort? Then ask in the comments. We will respond and may even address it in a future post!

The LTC2185 is a 125Msps 16-bit ADC with excellent noise and linearity performance while only consuming 185mW per channel. It is ideal for demanding low power applications that require excellent AC performance. A high performance ADC like the LTC2185 requires a high performance amplifier driving it to maintain the excellent performance. The ADA4927-1 delivers the linearity performance required by the LTC2185 while only consuming 215mW. The well designed package of the ADA4927-1 allows for a simple layout that reduces parasitic capacitance in the feedback path that can erode the phase margin of the amplifier. This combination of ADC and driver allows excellent performance from 62.5-125MHz a region where other high speed amplifiers are lacking. 


The LTC2185 is a two-channel simultaneous sampling parallel ADC which offers a choice of full-rate CMOS, or double data rate (DDR) CMOS/LVDS digital outputs. Pin-compatible speed grade options include 25Msps, 40Msps, 65Msps, 80Msps and 105Msps with approximate power dissipation of just 1.5mW/Msps per channel. It includes popular features such as the digital output randomizer and alternate bit polarity (ABP) mode that minimize digital feedback when using parallel CMOS outputs.  Analog full power bandwidth of 550MHz and ultralow jitter of 0.07psRMS allows under-sampling of IF frequencies with excellent noise performance. To maintain this level of performance the LTC2185 needs to be driven with an appropriate amplifier like the ADA4927-1.


The ADA4927 is a high speed differential current feedback amplifier. Fabricated on Analog Devices’ silicon-germanium process, the ADA4927-1 has excellent distortion and an input voltage noise of only 1.3nV/rtHz. This allows it to drive high speed ADCs like the LTC2185. The gain of the ADA4927-1 is set with external feedback resistors located next to the input pins.  By keeping the feedback pins and input pins close on the package, the ADA4927-1 provides a clean layout and minimizing the parasitic capacitance in the feedback network. This make the ADA4927-1 an ideal choice for driving high performance ADCs, like the LTC2185, from DC to 125 MHz.


Figure 1 shows a schematic of the ADA4927-1 driving the LTC2185. The corresponding layout is shown in figure 2.  The feedback pins on the ADA4927-1 are adjacent to the input pins which minimizes the parasitic capacitance of the feedback node and improves the phase margin of the amplifier. It also Simplifier the layout by making it possible to place feedback resistors directly across the two pins and not having additional trace length in the feedback path. There is a simple filter between the amplifier and ADC that reduces the wideband noise of the amplifier and improves the SNR of the system. This filter also attenuates the sampling glitches from the ADC before they reach the amplifier. This helps keep the output network of the ADA4927 from oscillating in response to these glitches. This filter network can be modified to accommodate a wide range of input bandwidth requirements.

Figure 1:  Schematic showing an ADA4927-1 driving one channel of the LTC2185


Figure 2:  Layout showing an ADA4927-1 driving once channel of the LTC2185


Figure 3 and figure 4 show the SNR and SFDR of the LTC2185 and ADA4927-1 combination. The SFDR stays above 67dB out to 125MHz while the SNR is better than 63dB to the same frequency. This combination only consumes 250mW. With a sample rate of 125Msps, this combination provides good performance through the entire 2nd Nyquist zone where other amplifiers begin to have poor linearity. 


Figure 3:  SNR of the LTC2185 driven with the ADA4927-1

Figure 4:  SFDR of the LTC2185 driven with the ADA4927-1


Using the ADA4927-1 to drive the LTC2185 provides excellent linearity while keeping the power consumption low. The fact that the ADA4927-1 stays very linear out to 125MHz allows this ADC amplifier combination to be used in demanding communication and medical applications that require the use of the second Nyquist zone of the LTC2185. The pin out of the ADA4927-1 and filter design minimize the complexity of the layout while maintaining excellent performance on a low power budget.


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