Tag Archives: Atmel microcontrollers

Arduino in research and biotech

Arduino’s acceptance into the biotech research community is evident from its increasing mentions in high-profile science and engineering journals. Mentions of Arduino in these journals alone have gone from zero to more than 150 in just in the last two years.

While it may be best known as staple for hobbyists, Makers, and hackers who build on their own time, Arduino and Atmel have a strong and rapidly growing following among professional engineers and researchers.

For biotech researchers like myself, experimental setups often require highly specific instruments with strict design rules for parameters such as timing, temperature, motion, force/pressure, and light. Such specific instruments would be time-consuming and expensive to have custom built, as the desired experimental conditions often change as we investigate different samples, cell types, etc. Here, Atmel chips and Arduino boards find a nice niche for making your own affordable, custom setups that are repeatable, precise, and automated. Arduino and Atmel provide microcontrollers in a myriad of form factors, I/O options, and connectivity that are available from a number of vendors. Meanwhile, freeware Arduino code and hardware drivers are also available with many sensors and actuators to go with your board. Best of all, Arduino is designed for a wide audience and range of experiences, making it easy to use for a variety of projects and complexities. So as experimental conditions or goals change, your hardware can easily be re-purposed and re-programmed according to specifications.

Arduino’s acceptance into the biotech research community is evident from its increasing mentions in high profile journals in science and engineering including Nature Methods, Proceedings of the National Academy of the SciencesLab on a Chip, Cell, Analytical Chemistry, and the Public Library of Science (PLOS). Mentions of Arduino in these journals alone have gone from zero to more than 150 in just in the last two years.

In recent years, Arduino-powered methods have started to appear in a variety of cutting edge biotechnology applications. One prominent example is optogenetics, a field in which engineered sequences of genes can be turned on and off using light. Using Arduino-based electronic control over lights and motors, researchers have constructed tools to measure how the presence or absence of these gene sequences can produce different behaviors in human neurons [1][6][7] or in bacterial cells [2]. Light and motor control has also allowed for rapid sorting of cells and gene sequences marked with fluorescent dyes, which can be detected by measuring light emitted to photodiodes. While the biology driving this research is richly complex and unexplored, the engineering behind the tools required to observe and measure these phenomena are now simple to use and well-characterized.

Neuroscientists Voights, Sanders, and Newman at the Open Ephys project provide walkthroughs and add-ons for using Arduino to help them create tools for probing cells.  From left to right, Arduino-based hardware for creating custom electrodes, providing multi-channel input to neurons, and for control over optogenetic lighting circuits.  [6],[7]

Neuroscientists Voights, Sanders, and Newman at the Open Ephys project provide walkthroughs and add-ons for using Arduino to help them create tools for probing cells. From left to right, Arduino-based hardware for creating custom electrodes, providing multi-channel input to neurons, and for control over optogenetic lighting circuits. [6],[7]

Neuroscientists Voights, Sanders, and Newman at the Open Ephys project provide walkthroughs and add-ons for using Arduino to help them create tools for probing cells. From left to right, Arduino-based hardware for creating custom electrodes, providing multi-channel input to neurons, and for control over optogenetic lighting circuits. [6],[7]

Arduino-based automation can be used for supplanting a number of traditional laboratory techniques including control of temperature, humidity, and/or pressure during cell culture conditions; monitoring cell culturing through automated sampling and optical density measurements over time; neurons sending and receiving electrochemical signals; light control and filtration in fluorescence measurements; or measurement of solution salinity. This kind of consistent, automated handling of cells is a key part of producing reliable results for research in cell engineering and synthetic biology.

Synthetic biologists Sauls et al. provide open-source schematics for creating an Arduino-powered turbidostat to automate the culturing of cells with recombinant genes. [5]

Synthetic biologists Sauls et al. provide open-source schematics for creating an Arduino-powered turbidostat to automate the culturing of cells with recombinant genes. [5]

Synthetic biologists Sauls et al. provide open-source schematics for creating an Arduino-powered turbidostat to automate the culturing of cells with recombinant genes. [5]

Arduino has also found an excellent fit in the microfluidics communityMicrofluidics is the miniaturization of fluid-handling technologies—comparable to the miniaturization of electronic components. The development of microfluidic technologies has enabled a myriad of technical innovations including DNA screening microchips, inkjet printers, and the screening and testing of biological samples into compact and affordable formats (often called “lab on a chip” diagnostics) [3]. Their use often requires precise regulation of valves, motors, pressure regulation, timing, and optics, all of which can be achieved using Arduino. Additionally, the compact footprint of the controller allows it to be easily integrated into prototypes for use in medical laboratories or at the point of care. Recent work by the Collins and Yin research groups at MIT has produced prototypes for rapid, point-of-care Ebola detection using paper microfluidics and an Arduino-powered detection system [4].

Microfluidic devices made from paper (left) or using polymers (right) have been used with Arduino to create powerful, compact medical diagnostics (Left: Ebola diagnostic from Pardee et. Al [4], Right: Platelet function diagnostic from Li et al. [9])

Microfluidic devices made from paper (left) or using polymers (right) have been used with Arduino to create powerful, compact medical diagnostics (Left: Ebola diagnostic from Pardee et. Al [4], Right: Platelet function diagnostic from Li et al. [9])

Microfluidic devices made from paper (left) or using polymers (right) have been used with Arduino to create powerful, compact medical diagnostics (Left: Ebola diagnostic from Pardee et. Al [4], Right: Platelet function diagnostic from Li et al. [9])

Finally, another persistent issue in running biological experiments is continued monitoring and control over conditions, such as long-term time-lapse experiments or cell culture.   But what happens when things go wrong? Often this can require researchers to stay near the lab to check in on their experiments. However, researchers now have access to on-board wi-fi control boards [8] that can send notifications via email or text when their experiments are completed or need special attention.  This means fewer interruptions, better instruments, and less time spent worrying about your setup.

The compact Arduino Yun microcontroller combines the easy IDE of Arduino with the accessibility of built-in wi-fi to help you take care of your experiments remotely [8]

The compact Arduino Yun microcontroller combines the easy IDE of Arduino with the accessibility of built-in wi-fi to help you take care of your experiments remotely [8]

True to Arduino’s open-source roots, the building, use, and troubleshooting of the Arduino-based tools themselves are also available in active freeware communities online [5]–[7].

Simply put, Arduino is a tool whose ease of use, myriad applications, and open-source learning tools have provided it with a wide and growing user base in the biotech community.

Melissa Li is a postdoctoral researcher in Bioengineering who has worked on biotechnology projects at UC Berkeley, the Scripps Research Institute, the Massachusetts Institute of Technology, Georgia Institute of Technology, and the University of Washington. She’s used Arduino routinely in customized applications in optical, flow, and motion regulation, including a prototype microfluidic blood screening diagnostic for measuring the protective effects of anti-thrombosis medications [9], [10]. The opinions expressed in this article are solely her own and do not reflect those of her institutions of research.

[1]       L. J. Bugaj, A. T. Choksi, C. K. Mesuda, R. S. Kane, and D. V. Schaffer, “Optogenetic protein clustering and signaling activation in mammalian cells,” Nat. Methods, vol. 10, no. 3, pp. 249–252, Mar. 2013.

[2]       E. J. Olson, L. A. Hartsough, B. P. Landry, R. Shroff, and J. J. Tabor, “Characterizing bacterial gene circuit dynamics with optically programmed gene expression signals,” Nat. Methods, vol. 11, no. 4, pp. 449–455, Apr. 2014.

[3]       E. K. Sackmann, A. L. Fulton, and D. J. Beebe, “The present and future role of microfluidics in biomedical research,” Nature, vol. 507, no. 7491, pp. 181–189, Mar. 2014.

[4]       K. Pardee, A. A. Green, T. Ferrante, D. E. Cameron, A. DaleyKeyser, P. Yin, and J. J. Collins, “Paper-Based Synthetic Gene Networks,” Cell.

[5]       “Evolvinator – OpenWetWare.” [Online]. Available: http://openwetware.org/wiki/Evolvinator. [Accessed: 12-Jan-2015].

[6]       “Open Ephys,” Open Ephys. [Online]. Available: http://www.open-ephys.org/. [Accessed: 12-Jan-2015].

[7]       Boyden, E. “Very simple off-the-shelf systems for in-vivo optogenetics”. http://syntheticneurobiology.org/protocols/protocoldetail/35/9 [Accessed: 12-Jan-2015].

[8]       “Arduino Yun”. http://arduino.cc/en/Guide/ArduinoYun [Accessed: 12-Jan-2015].

[9]       “Can aspirin prevent heart attacks? This device may know the answer,” CNET. [Online]. Available: http://www.cnet.com/news/can-aspirin-prevent-heart-attacks-this-device-may-know-the-answer/. [Accessed: 12-Jan-2015].

[10]       M. Li, N. A. Hotaling, D. N. Ku, and C. R. Forest, “Microfluidic thrombosis under multiple shear rates and antiplatelet therapy doses.,” PloS One, vol. 9, no. 1, 2014.


Fusing fashion and tech with an Atmel powered robotic dress

A collaboration between 360 Fashion Network CEO Anina Net, Polish couture designer Michal Starost and IT architect Bruce Bateman has led to world’s first robotic dress powered by Atmel microcontrollers (MCUs).


The dress made its catwalk debut at the “When Technology Meets Fashion” event held during Beijing Design Week.

Aside from an Atmel MCU, the robotic garment features 6 servo-controlled support arms comprised of fiberglass reinforced with aluminum, custom software and a high-powered battery pack. In what sounds like something out of Hunger Games, the arms lift in sync to convert the dress from a day dress to an evening gown.

While the current version of the dress is not Internet-connected and does not employ any sensors, we can surely expect to see further advancements in coming months.


In fact, Anina says 360Fashion Network is currently on another version that will be smartphone-controlled to lift the dress to the wearer’s desired length. Additionally, future iterations may even monitor vital signs and change color or form depending on the body temperature or heart rate of the wearer.

“More advanced iterations might also communicate with networked databases, adapting the color, weave, pattern, length, and style of the dress based on real-time information on new cultural trends, environmental changes, news developments, or weather conditions. The speed at which such innovations can be realized, however, will depend heavily on progress in fabric technology,” the company wrote in a recent blog post.


This robotic dress wasn’t the only eye-opening garment exhibited during Design Week. The team at 360Fashion Network also unveiled four dresses that integrated lasers into the fabric as a key design element.

From an [ATmega32u4 based] Katniss Everdeen LED dress to an [Arduino powered] personal space skirt, we are only at the mere beginning of fashion and technology’s coalescence. Like stylists to the latest trends, we’ll be there to piece together these next-gen creations using an assortment of 8- and 32-bit MCUs.



ARM unveils 32-bit Cortex-M7 processor for the Internet of Things

ARM has unveiled a new 32-bit Cortex-M processor that delivers double the compute and digital signal processing (DSP) capability of today’s most powerful ARM-based MCUs. The ARM Cortex-M7 is targeted at high-end embedded applications used in next generation vehicles, connected devices, and smart homes and factories. Atmel has been named one of the early lead licensees of the Cortex-M7 processor, enabling us to deliver exciting new products to the market in the forthcoming months.


“The addition of the Cortex-M7 processor to the Cortex-M series allows ARM and its partners to offer the most scalable and software-compatible solutions possible for the connected world,” explained Noel Hurley, General Manager of ARM’s CPU Group. “The versatility and new memory features of the Cortex-M7 enable more powerful, smarter and reliable microcontrollers that can be used across a multitude of embedded applications.”

The Cortex-M7 achieves an impressive 5 CoreMark/MHz. This performance allows the Cortex-M7 to deliver a combination of high-performance and digital signal control functionality that will enable MCU silicon manufacturers to target highly demanding embedded applications — including next-generation vehicles, connected devices and smart homes —  while keeping development costs low. System designers can therefore take advantage of extensive code reuse which in turn offers lower development and maintenance costs. Through these products, the benefits delivered by the Cortex-M7 processor will be evident in our increasingly connected world.

Cortex-M7 summary

Enabling faster processing of audio and image data and voice recognition, the benefits delivered by the Cortex-M7 processor will be immediately apparent to users. The core also provides the same C-friendly programmer’s model and is binary compatible with existing Cortex-M processors. Ecosystem and software compatibility offers simple migration from any existing Cortex-M core to the new Cortex-M7.

“The Cortex-M7 is well positioned between Atmel’s Cortex-M based MCUs and Cortex-A based MPUs enabling Atmel to offer an even greater range of processing solutions,” said Reza Kazerounian, Atmel Senior Vice President and General Manager, MCU Business Unit. “Customers using the Cortex-M-based MCU will be able to scale up performance and system functionality, while keeping the Cortex-M class ease-of-use and maximizing software reuse. We see the ARM Cortex-M7 addressing high-growth markets like IoT and wearables, as well as automotive and industrial applications that can leverage its performance and power efficiency.”

WhiteGoods cortex-M7

In today’s connected world, future devices will be getting smarter in order to operate more efficiently using minimal energy and resources. As ARM notes in its blog, these next generation products are moving to more sophisticated displays, advanced touchscreen panels, and advanced control motors to include field-oriented control algorithms in their motor driver control in order to operate more efficiently. Some of these also need to run communications software stacks to interface with other appliances and interface with the outside world to provide billing information, power usage and maintenance information.

All of these requirements demand more performance from a microcontroller, which lies at the heart of the appliance… and Cortex-M7 based MCUs will deliver that performance.

“The day the refrigerator talks to the milk carton, that’s in a gimmicky category. But to have the dishwasher and refrigerator coordinate their cycles to reduce the electricity load — that becomes useful,” ARM CEO Simon Segars told Reuters.


Key features of the ARM Cortex-M7 core include:

  • Six stage, superscalar pipeline delivering 2000 Coremarks at 400MHz in a 40LP process
  • AXI interconnect (supports 64-bit transfer) and fully integrated optional caches for instruction and data allowing efficient access to large external memories and powerful peripherals
  • Tightly coupled memory interfaces for rapid, real-time response
  • Extensive implementation configurability to enable a wide range of cost and performance points to be targeted
  • Optional full instruction and data trace via the Embedded Trace Macrocell enabling greater system visibility
  • An optional safety package and built-in fault detection features contribute toward ASIL D and SIL 3 compliance, meaning Cortex-M7 is the perfect choice for companies targeting safety-related markets including automotive, industrial, transport and medical applications
  • Widest third-party tools, RTOS, middleware support of any architecture, provided by the ARM Connected Community of complementary partner companies.


From building automation to smart metering to wearables and other Internet of Things (IoT) applications, a new generation of connected products are increasingly powering our lifestyle. Internet and wireless enabled devices embedded with processors give these once-ordinary “things” new powers. Atmel continues to make it easy for designers to create a more intelligent, more connected world through its Atmel | SMART family. This lineup of ARM-based MCUs drive smart, connected devices in the era of IoT, wireless, and energy efficiency. These solutions include embedded processing and connectivity — as well as software and tools — designed to make it faster and more cost-effective to bring smart products to market. Atmel | SMART MCUs combine powerful 32-bit ARM cores with industry-leading low-power technology and intelligent peripherals.

To learn more about the newly-unveiled, high-performance processor, you can read ARM’s entire press release here.

1,024 tiny robots assemble into shapes like intelligent insects

Researchers in Harvard’s Self-Organizing Systems Research Group have introduced Kilobots — a 1,024-strong swarm of decentralized cooperating robots that can assemble themselves into complex shapes with very little human input.


A team comprised of Michael Rubenstein, Alejandro Cornejo, and Professor Radhika Nagpal have described their 1,024-robot swarm in a detailed study published in Science“Each robot has the basic capabilities required for a swarm robot, but is made with low-cost parts, and is mostly assembled by an automated process. In addition, the system design allows a single user to easily and scalably operate a large Kilobot collective, such as programming, powering on, and charging all robots systems,” the researchers explain.

The thousand plus bots are each embedded with an Atmel microcontroller, two vibrating motors powering rigid legs that allow them to skitter across smooth surfaces, and an infrared emitter-sensor pair to receive commands and communicate wirelessly. They can transform into a variety of shapes, including a starfish and the letter K (as seen below).


What makes this piece of work so exceptional is that, before the Kilobot, most collectives were limited to less than 100 robots. In order to exceed previous limitations, this required completely rethinking how the robots were designed. To do this, the team of researchers created a coin-sized robot that possessed the ability to move on three stick-legs using two vibrating motors. It could then communicate with neighbouring robots using the aforementioned infrared light, signal its state by changing a color LED and sense ambient light.

Kilobot robots

In current robotics research, there has been a vast body of work on algorithms and control methods for groups of decentralized cooperating robots, called a swarm or collective. “These algorithms are generally meant to control collectives of hundreds or even thousands of robots; however, for reasons of cost, time, or complexity, they are generally validated in simulation only, or on a group of a few 10s of robots,” the study reveals. With the robots ready, the team developed an algorithm which could guarantee that a large numbers of robots, with limited capabilities and local communication, could cooperatively self-assemble into user-specified shapes. Four “seed” robots kick off the process, generating a domino-effect of signals that propagate through the rest of the swarm. How each Kilobot positions itself is dependent upon the distance between itself and its nearby bots. IEEE Spectrum explains that while in biological systems, swarms can organize and control themselves based on a set of very simple rules. With the Kilobots, however, the algorithm that they use to create shapes are based on a similarly simple set of capabilities:

  • Edge-following, where a robot can move along the edge of a group by measuring distances from robots on the edge
  • Gradient formation, where a source robot can generate a gradient value message that increments as it propagates through the swarm, giving each robot a geodesic distance from the source
  • Localization, where the robots can form a local coordinate system using communication with, and measured distances to, neighbors

“Increasingly, we’re going to see large numbers of robots working together, whether its hundreds of robots co-operating to achieve environmental clean up or a quick disaster response, or millions of self-driving cars on our highways. Understanding how to design ‘good’ systems at that scale will be critical,” said Professor Radhika Nagpal.

For those interested in making, buying or programming their own Kilobot swarm, you can check out Harvard’s official project page here.

Sewn open: Arduino and soft electronics

As several other recent threads on SemiWiki have pointed out, the term “wearables” is a bit amorphous right now. The most recognizable wearable endeavors so far are Google Glass, the smartwatch, and the fitness band, but these are far from the only categories of interest.

There is another area of wearable wonder beginning to get attention: clothing, which has drawn the interest of researchers, makers, and moms alike. The endgame as many see it is smart clothing: the weaving of electronics, sensors, and conventional fabrics into something called e-textiles. However, while athletes, soldiers, and other niches may get sensor-impregnated jerseys sooner, affordable clothing based on exotic advanced fabrics for most consumers may still be 20 or 30 years away by some estimates.

Right now, we have these anything-but-soft computing structures – chips, circuit boards, displays, switches – adaptable for some clothing applications. Still missing are some key elements, most notably power in the form of energy harvesting or smaller and denser batteries. The influence of water-based washing machines and their adverse effect on most electronics also looms large.

How do we cross this gap? It’s not all about advanced R&D; these types of challenges are well suited for experimentation and the imagination of makers. Several Arduino-compatible maker modules – all based on Atmel microcontrollers – have jumped in to the fray, showing how “soft electronics” can help create solutions.

LilyPad embroidery
Maybe I’ve built one or two too many harness assemblies using expensive, mil-spec circular connectors, but the fascinating thing to me is what makes all these boards wearable. Small size is nice, but anybody knows a project needs wiring, right? You’ll notice the large plated holes on the first several offerings: these are eyelets for conductive thread, literally intended to sew these boards to other components like fabric pushbuttons. Many projects also use snaps, similar to 9V battery connections, to disconnect boards for conventional washing of the garment.


The other side of this is the software. One of the attractive features of Arduino is the IDE, real live C-style programming simplified for the masses, with functions designed to perform I/O on the Atmel MCU. Code is edited on a PC or Mac, and compiled into a sketch and uploaded to the board. There are so many examples of code for Arduino maker modules out there available in open source, it makes it easy to find and integrate functions quickly.

If that all sounds crazy, consider the pioneer for this is Leah Buechley of the MIT Media Lab, one of the thought leaders of the maker movement and an expert on e-textiles. She is the brain behind the LilyPad, the original 2” diameter Arduino wearable circa 2007 commercialized through SparkFun, with the most recent version featuring the ATmega32u4 and native USB.

Adafruit took the next steps with two wearable boards.FLORA is slightly smaller than the LilyPad and retains the same familiar circular profile and ATmega32u4 MCU.GEMMA goes even smaller, 1.1” in diameter, packing an ATtiny85 on board with a USB connection for easy development.

Adafruit GEMMA

Not to be outdone by circles, squares and rectangles are still in the mix.SquareWear 2.0 comes in two versions, the 1.7” square variant with a coin cell socket onboard, both including the ATmega328 MCU with simulated USB, high current MOSFET ports, a light sensor, and a temperature sensor. Seeed grabbed the ATmega32u4 and designed it into the Xadow, a tiny 1” x 0.8” expandable unit with integrated flat cable connectors for daisy chaining.

SquareWear 2.0

These aren’t just toys for creating flashing LEDs; there is no shortage of sensors and connectivity, including displays, GPS, Bluetooth, and more compatible with these wearable maker modules. Their popularity is growing: Becky Stern of Adafruit claims there are over 10,000 units of FLORA shipped so far, and they are the darlings of maker faire fashion shows and hackathons.

Besides the upside for makers, maybe this sewing angle will finally allow us to explain electronics to our moms, after all. Until we get to the fulfilled flexible future of e-textiles and more advanced technology, the conductive thread of soft electronics will stitch together creative ideas using somewhat familiar tiny modules with today’s microcontrollers.

This post has been republished with permission from SemiWiki.com, where Don Dingee is a featured blogger. It first appeared there on May 21, 2014.

Baskin-Robbins only has 31 flavors, Atmel has 505

Actually these days even Baskin-Robbins has more, but not 505 like Atmel. That’s a lot. While some are AVR, both 8-bit and 32-bit, others are various flavors of ARM (all 32-bit) ranging from older parts like the ARM9 to various flavors of Cortex ranging from the M0 (tiny microcontroller with no pipeline or cache) up to A5. Of course, the ARM product line goes all the way up to 64-bit Cortex-A57 and so on — but they are not in any sense of the word microcontrollers and are really only used in SoCs and not standalone products.

But with 505 choices, how do you pick one? Fortunately, Atmel has made it easy for you to navigate the various flavors. With the help of the company’s MCU product finder, you now have the ability to input your hard constraints, while the tool will narrow down the choices. For example, if you want your microcontroller to have at least 64 Kbytes of flash, then there are only 257 out of the 505 that will suit your needs. For each parameter, users can set minimums and maximums — except for the yes/no choices.

When it comes to the selection process, there are several things that you can constrain:

  • Flash memory (0 to 2Mbytes)
  • Pin count (6 to 324)
  • Operating frequency (1 to 536MHz)
  • CPU architecture (pick from 8-bit AVR, 32-bit AVR, ARM 926 and 920, ARM Cortex M0, M3, M4, A5)
  • SRAM (30 bytes to 256 Kbytes)
  • EEPROM (none to 8 Kbytes)
  • Max I/O pins (4 to 160)
  • picoPower (yes or no)
  • Operating voltage (various ranges from 0.7V to 6V)
  • Operating temperature (various from -20oC to 150oC)
  • Number of touch channels (none to 256)
  • Number of timers (1 to 10)
  • Watchdog (yes or no)
  • 32KHz real time clock (yes or no)
  • Analog comparators (0 to 8)
  • Temperature sensor (yes or no)
  • ADC resolution (8 to 16 bits)
  • ADC channels (2 to 28)
  • DAC channels (0 to 4)
  • UARTs (0 to 8)
  • SPI (1 to 12)
  • TWI (aka I2C) interface (none to 6)
  • USB interface (none, device only, host+OTG, host and device)
  • PWM channels (0 to 36)
  • Ethernet interfaces (none to 2)
  • CAN interfaces (none to 2)

Wow, that’s a lot of options! But after a couple of dozen selections, you can narrow down your choice to something manageable. Here’s how the interface will appear:

Say for instance, I wanted to pick a microcontroller, an ARM Cortex of some flavor. Already choices are down to 189. I want 32K to 128K of flash (now down to 73 choices). I want it to run at an operating frequency of at least 64 MHz (now down to 10). I want 4K of SRAM (turns out all 10 choices already have that much). I need 4 timers. I am now down to 2 choices:

These two choices are the ATSAM3S1C and the ATSAM3S2C — both ARM Cortex-M3s. The first has 64K of flash and the second 128K. I can click on the little PDF icon and access a full datasheet for these microprocessors. If I don’t like the choices and I have some flexibility on specs, then obviously I can go back and play with the parameters to get some new options.

I can click on the “S” to order samples. However, in order to do this, you must already have an Atmel account. Or, with just another click on the shopping cart icon, I can obtain a list of distributors throughout various geographic regions, where I can actually place an order. It even tells me how many each of them have in stock!

For those of you ready to start searching, you can find the Atmel Microcontrollers Selector here.

This post has been republished with permission from SemiWiki.com, where Paul McLellan is a featured blogger. It first appeared there on March 2, 2014.