The Future of Temperature-Compensated Crystal Oscillators

Most modern digital and analog electronic devices require a time base to perform their intended function. Found in everything from cell phones to smart munitions, quartz crystal oscillators are widely used in many embedded applications. Quartz resonators’ high Q, excellent temperature performance, and superior long-term aging makes them the clear resonator of choice for many applications. The frequency versus temperature performance of a discrete LC oscillator will be on the order of several hundred parts per million (ppm) per °C, where a crystal oscillator (XO) will have roughly ±30 ppm over the entire industrial temperature range (–40 to +85°C). While being superior to a discrete oscillator, this temperature stability is not nearly sufficient for many modern applications.

EsterlineFigure1

Source: John Esterline

The temperature-compensated crystal oscillator (TCXO) employs the use of an open loop compensation circuit to create a correction voltage to reduce the inherent frequency versus temperature characteristic of the crystal. The crystals used in TCXOs have frequency versus temperature characteristics that approximate a third-order polynomial, as seen in the nearby figure.

The early designs for TCXOs employed a network of thermistors and resistors to create a correction voltage. By using thermistors with different slopes and properly selecting the fixed value resistors, the correction voltage can be made to have a shape factor matched to the crystal’s frequency versus temperature performance. The correction voltage is applied to a varactor in the feedback path of the TCXO. This change in capacitance in the feedback path alters the tuning of the oscillator, thus changing the output frequency and compensating it for temperature effects. Thermistor/Resistor network TCXOs can achieve frequency versus temperature stabilities of around ±1 ppm over the industrial temperature range; however, they are limited in their curve-fitting capabilities because of the nature of using discrete thermistors and resistors.

Thermistor/resistor network TCXOs are still found in specialized environments including satellite and other space applications where modern solid-state devices do not have the radiation hardness to survive. Most TCXOs manufactured today utilize an ASIC which contains the oscillator circuit and a third- or fifth-order polynomial voltage generator. The polynomial generator is an analog output voltage but also has digital registers for setting the coefficients of the polynomial. The newest generations of TCXO ASICs can provide temperature performances of ±0.1 ppm over the industrial temperature range. This is a 10-fold improvement over what is obtainable with a traditional thermistor/resistor network TCXOs and also has the advantage of a much smaller footprint (5 mm × 3.2 mm).

Some high-precision applications require frequency versus temperature stabilities better than ±0.1 ppm. To meet these challenging specifications a different methodology is implemented. An oven-controlled crystal oscillator (OCXO) uses a heater circuit and thermal insulation to keep the crystal at an elevated temperature (≈15°C above the upper operating temperature limit). By controlling the crystal’s temperature and keeping it nearly constant, the frequency deviation due to ambient temperature changes is vastly reduced. OCXOs can achieve frequency versus temperature stabilities of ±0.005 ppm. This improved performance comes at the cost of a larger footprint and increased power consumption. The TCXO’s performance limit of ±0.1 ppm is due to several factors. First, the resonators are not perfect. Their frequency versus temperature stability approximates a third-order polynomial; however, higher order effects are present. Secondly, the polynomial generator is nonideal and induces some higher order artifacts, leaving the user with residuals of ±0.1 ppm. A new methodology which uses an artificial neural network (ANN) to create the correction voltage has recently been demonstrated. The ANN is superior in that the neural network is not inherently shape limited like a third-order polynomial. If enough data is presented to the ANN, it can “learn” the crystal’s temperature performance shape and correct for it. This new methodology has been shown to provide ±0.01 ppm frequency versus temperature stability over the industrial range. The ANN algorithm can achieve OCXO temperature performance in a much smaller footprint, and without the need for the power-hungry oven.

The evolution of quartz crystal time bases over the last 70 years has seen the frequency versus temperature stability improve by a factor of several thousand. As our need for more stable oscillators in smaller packages with less power consumption grows, the development of better compensation schemes is paramount. The ANN demonstrates a technology that has much potential. Its ability to adapt and change its shape factor makes it ideal for complex compensation problems.

EsterlinePhotoJohn Esterline is the CEO of Esterline Research and Design, LLC, a Pennsylvania based start-up company. John holds an MEngEE and a BSEE from Pennsylvania State University. His research interests focus on temperature compensation algorithms for the improvement of embedded time bases. John is the inventor on two US patents (US8188800 B2, US8525607 B2), and the inventor of one patent pending (US 13/570,563). Esterline Research and Design, LLC offers consulting services in frequency control, test and automation and other subject matter in addition to its RF testing products.

 

Circuit Cellar 291 (October 2014) is now available.

Self-Reconfiguring Robotic Systems & M-Blocks

Self-reconfiguring robots are no longer science fiction. Researchers at MIT are rapidly innovating shape-shifting robotic systems. In the August 2014 issue of Circuit Cellar, MIT researcher Kyle Gilpin presents M-Blocks, which are 50-mm cubic modules capable of controlled self-reconfiguration.

The creation of autonomous machines capable of shape-shifting has been a long-running dream of scientists and engineers. Our enthusiasm for these self-reconfiguring robots is fueled by fantastic science fiction blockbusters, but it stems from the potential that self-reconfiguring robots have to revolutionize our interactions with the world around us.

Source: Kyle Gilpin

Source: Kyle Gilpin

Imagine the convenience of a universal toolkit that can produce even the most specialized tool on demand in a matter of minutes. Alternatively, consider a piece of furniture, or an entire room, that could change its configuration to suit the personal preferences of its occupant. Assembly lines could automatically adapt to new products, and construction scaffolding could build itself while workers sleep. At MIT’s Distributed Robotics Lab, we are working to make these dreams into reality through the development of the M-Blocks.

The M-Blocks are a set of 50-mm cubic modules capable of controlled self-reconfiguration. Each M-Block is an autonomous robot that can not only move independently, but can also magnetically bond with other M-Blocks to form larger reconfigurable systems. When part of a group, each module can climb over and around its neighbors. Our goal is that a set of M-Blocks, dispersed randomly across the ground, could locate one another and then independently move to coalesce into a macro-scale object, like a chair. The modules could then reconfigure themselves into a sphere and collectively roll to a new location. If, in the process, the collective encounters an obstacle (e.g., a set of stairs to be ascended), the sphere could morph into an amorphous collection in which the modules climb over one another to surmount the obstacle.  Once they have reached their final destination, the modules could reassemble into a different object, like a desk.

The M-Blocks move and reconfigure by pivoting about their edges using an inertial actuator. The energy for this actuation comes from a 20,000-RPM flywheel contained within each module. Once the motor speed has stabilized, a servomotor-driven, self-tightening band brake decelerates the flywheel to a complete stop in 15 ms. All of the momentum that had been accumulated in the flywheel is transferred to the frame of the M-Block. Consequently, the module rolls forward from one face to the next, or if the flywheel velocity is high enough, it rapidly shoots across the ground or even jumps several body lengths through the air. (Refer to www.youtube.com/watch?v=mOqjFa4RskA  to watch the cubes move.)

While the M-Blocks are capable of independent movement, their true potential is only realized when many modules operate as a group. Permanent magnets on the outside of each M-Block serve as un-gendered connectors. In particular, each of the 12 edges holds two cylindrical magnets that are captive, but free to rotate, in a semi-enclosing cage. These magnets are polarized through their radii, not through their long axes, so as they rotate, they can present either magnetic pole. The benefit of this arrangement is that as two modules are brought together, the magnets will automatically rotate to attract. Furthermore, as one and then two additional M-Blocks are added to form a 2 × 2 grid, the magnets will always rotate to realign and accommodate the additional modules.

The same cylindrical magnets that bond neighboring M-Blocks together form excellent pivot axes, about which the modules may roll over and around one another. We have shown that the modules can climb vertically over other modules, move horizontally while cantilevered from one side, traverse while suspended from above, and even jump over gaps. The permanent magnet connectors are completely passive, requiring no control and no planning. Because all of the active components of an M-Block are housed internally, the modules could be hermetically sealed, allowing them to operate in extreme environment where other robotic systems may fail.

While we have made significant progress, many exciting challenges remain. In the current generation of modules, there is only a single flywheel, and it is fixed to the module’s frame, so the modules can only move in one direction along a straight line. We are close to publishing a new design that enables the M-Blocks to move in three dimensions, makes the system more robust, and ensures that the modules’ movements are highly repeatable. We also hope to build new varieties of modules that contain cameras, grippers, and other specialized, task-specific tools. Finally, we are developing algorithms that will allow for the coordinated control of large ensembles of hundreds or thousands of modules. With this continued development, we are optimistic that the M-Blocks will be able to solve a variety of practical challenges that are, as of yet, largely untouched by robotics.

Kyle Gilpin

Kyle Gilpin

ABOUT THE AUTHOR

Kyle Gilpin, PhD, is a Postdoctoral Associate in the Distributed Robotics Lab at the Massachusetts Institute of Technology (MIT) where he is collaborating with Professor Daniela Rus and John Romanishin to develop the M-Blocks. Kyle works to improve communication and control in large distributed robotic systems. Before earning his PhD, Kyle spent two years working as a senior electrical engineer at a biomedical device start-up. In addition to working for MIT, he owns a contract design and consulting business, Crosscut Prototypes. His past projects include developing cellular and Wi-Fi devices, real-time image processing systems, reconfigurable sensor nodes, robots with compliant SMA actuators, integrated production test systems, and ultra-low-power sensors.

Circuit Cellar 289 (August 2014) is now available.

Wearable Medical Computing and the Amulet Project

Health care is one of the most promising areas for employing wearable devices. Wearable mobile health sensors can track activities (e.g., count steps or caloric expenditure), monitor vital signs including heart rate and blood pressure, measure biometric data (e.g., glucose levels and weight), and provide alerts to medical emergencies including heart failures, falls, and shocks.

Applying wearable computing to support mobile health (mHealth) is promising but involves significant risks. For instance, there are security issues related to the reliability of the devices and sensors employed, the accuracy of the data collected, and the privacy of sensitive information.

The Amulet bracelet-style prototype for developers enables users to control its settings

The Amulet bracelet-style prototype for developers enables users to control its settings

Under the federally funded Amulet project, an interdisciplinary team of Dartmouth College and Clemson University researchers is investigating how wearable devices can effectively address medical problems while ensuring wearability, usability, privacy, and security for mHealth applications. The project aims to develop pieces of “computational jewelry” and a software framework for monitoring them. This computational jewelry set comprises wearable mobile health devices collectively named Amulet. An Amulet device could be worn as a discreet pendant or bracelet that would interact with other wearable health sensors that constitute the wearer’s wireless body-area network (WBAN). The Amulet device would serve as a “hub,” tracking health information from wearable health sensors and securely sending data to other health devices or medical professionals.

The project’s goals are multifold. Regarding the hardware, we’re focusing on designing small and unobtrusive form factors, efficient power sources, and sensing capabilities. With respect to the software, we’re concentrating on processing and interpreting the digital signs coming from the sensors, effectively communicating and synchronizing data with external devices, and managing encrypted data.

Amulet’s multiprocessor hardware architecture includes an application processor that performs computationally intensive tasks and a coprocessor that manages radio communications and internal sensors. Amulet’s current prototypes contain an accelerometer and a gyroscope to monitor the wearer’s motion and physical activities, a magnetometer, a temperature sensor, a light sensor, and a microphone. To save power, the application processor is powered off most of the time, while the coprocessor handles all real-time device interactions.

By employing event-driven software architecture, Amulet enables applications to survive routine processor shutdowns. Amulet is reactive, running only when an event of interest occurs. To handle such events, programmers can define their application as a finite-state machine and set appropriate functions. Amulet’s architecture enables applications to identify the computational states that should be retained between events. Explicitly managing program state (rather than implicitly managing state in a thread’s run-time stack) enables the run-time system to efficiently save the application state to persistent memory and power down the main processor without harming applications.

Amulet provides a secure solution that ensures the accuracy and the integrity of the data sensed and transmitted, continuous availability of the services provided (e.g., data sensing and processing and sending alerts and notifications), and access to the device’s data and services only by authorized parties after their successful authentication. Two key features enable Amulet to provide security in mHealth applications: sandboxing and the authorization manager. The former enforces access control, protects memory, and restricts the execution of event handlers. The latter enables applications to run small tasks until their completion, managing all resources by receiving requests and forwarding them to a corresponding service manager.

Amulet also aims to protect privacy, enabling users to control what is sensed and stored, where it is stored, and how it is shared (with whom). Amulet devices use privacy policies to protect patients’ sensitive information, which ensures confidentiality through authorized access and controlled sharing.

To guarantee easy wearability, the Amulet team focuses on understanding the user’s wishes, needs, and requirements and translating them into appropriate design decisions. Amulet provides a list of principles and guidelines for wearability, which will aid designers in providing high levels of comfort, aesthetics, ergonomics, and discretion in their projects.

Amulet includes a framework to support stakeholders involved in similar projects during all phases of development. It is intended to aid developers and designers from industry or academia. Amulet provides a general-purpose solution for body-area mobile health, complementing the capabilities of a smartphone and facilitating the development of applications that integrate one or more mHealth wearable devices.

Amulet also provides intuitive interfaces and interaction methods for user input and output, employing multimodal approaches that include gestures and haptics. Amulet has developed and continues to refine bracelet-style prototypes with a variety of envisioned applications, including: emergency responders (e.g., providing immediate notifications and quick responses in medical emergencies), stress monitoring, smoking cessation, diet (e.g., bite counting), and physical therapy (e.g., knee sensors).

Dr. Vivian Genaro Motti

Dr. Vivian Genaro Motti

ABOUT THE AUTHOR

Dr. Vivian Genaro Motti holds a PhD in Human Computer Interaction from the Université catholique de Louvain in Belgium. She is a Postdoctoral Research Fellow in the School of Computing at Clemson University in Clemson, SC. She works on the Amulet project, which is funded by a three-year, $1.5 million grant from the National Science Foundation’s Computer Systems Research program. As part of the Amulet project, Vivian is investigating how to properly ensure wearability and privacy in wearable applications for mobile health. Vivian has a BA in Biomedical Informatics and an MS in Human Computer Interaction from University of Sao Paulo in Brazil. Her main research interests are human computer interaction, medical applications, wearable devices and context awareness.

This appears in Circuit Cellar 288, July 2014.

The Future of Open-Source Hardware for Medical Devices

Medical technology is changing at a rapid pace, but regulatory compliance is also becoming increasingly harder. Regulatory compliance can act as a barrier to innovation, but it is a necessary check to ensure quality medical care. For small companies, aligning innovation with regulatory compliance can only help.

Fergus Dixon

When designing any new product, the FDA-recommended process is a great reference. First, the design input requirements must be written down. After the device has been designed and prototyped, verification and validation (V&V) will ensure that the device meets the design input. The device is then documented, creating the design output or device master record (DMR). Each device made is checked against the DMR and documented in the device history record (DHR). So all the details on how to make the device are contained in the DMR, and the results and traceability are recorded in the DHR.

My company recently asked an overseas company to design and manufacture an existing product. After many e-mails, the overseas company managed to build a working unit and immediately requested an order for 1,000. Before ordering even one unit, there was the matter of V&V. So what is V&V? Verification is the act of ensuring that the circuit acts as it should, as the circuit designer intended. This involves testing to a predetermined criteria, where the pass/fail is clearly defined. Testing happens by varying the inputs and checking the outputs to test the device as close to 100% as reasonably possible. When the inputs fall outside a normal range (e.g., a 10-VDC instead of 12-VDC battery voltage), the device must still work or it must provide a message showing why the device will not work (e.g., low battery light). Validation is the act of ensuring the circuit works as the customer or patient requires. This involves field testing, feedback, and rework—lots of it.

Working for medical device companies can be very rewarding. Smaller companies tend to work at the cutting edge. Larger companies are more secure and have stable products, but they can be less agile. With one company, we had a device that used smart batteries. During testing, we discovered that the batteries would not charge below 15ºC. After many meetings and e-mails to the manufacturers, the problem went to management, who decided to change the manual to say: “Do not charge below 15ºC.” Smaller dynamic companies can attract the best scientists, which is great until a connector fails and there is a roomful of highly intelligent people with no soldering iron experience. Every technology company can benefit from having at least one experienced technician or engineer. A few hours spent playing with an Arduino is a great way to get this experience.

What about open-source hardware (OSHW) for medical devices? For home hobbyists and students, OSHW is great. There is free access to working circuits, programs, and sketches. C compilers, which once cost several thousand dollars, are mostly free. For the manufacturers, the benefits are plenty of feedback, which can be used to improve products. There is one roadblock, and that involves the loss of intellectual property (IP), which means anyone can copy the hardware. Creative Commons has addressed this with an agreement that any copies must reference the original work. Closed-source hardware can also be good and present fewer issues with losing IP. Apple is a great example. Rather than use feedback to improve products, it makes smart decisions about future products. The iOS vs. Android battle can be viewed as a closed-source vs. open-source struggle that still hasn’t produced a winner. Medical devices and OSHW will have to meet up sometime.

Fergus Dixon’s embedded DNA sequencer project (Source: F. Dixon)

What about the future of medical devices? Well, the best is yet to come with brighter organic light-emitting diode (OLED) displays, a multitude of wireless connectivity options (all using the serial interface), and 32-bit ARM cores. DNA is gradually being unlocked with even “junk DNA” becoming meaningful. The latest hot topics of 3-D printing and unmanned aerial vehicles (UAVs) have direct medical applications with 3-D printed prosthetic ears and medical nanorobotics ready to benefit from UAV technology. Using a new sensor (e.g., a gyroscope) now means visiting an online seller such as Pololu, which offers ready-built development kits at reasonable prices. A recent design was a manually assisted CPR device project, which was abandoned due to lack of funding. How great would it be to have a device that could not only improve the current 10% survival rate with CPR (5% without CPR) but also could measure a patient’s health to determine whether CPR was helping and, even more importantly, when to stop administering it? Now that would be a good OSHW project.