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Xilinx snags LEAP Gold in Embedded Computing category

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The winners of the 2020 LEAP Awards (Leadership in Engineering Achievement Program) were announced last week in a digital ceremony, with products across 12 categories, including embedded computing, power electronics, and test and measurement. Critical to LEAP’s success is the involvement of the engineering community. No one at WTWH Media selected the winners. Instead, our editorial team did the arduous work of assembling a top-notch independent judging panel, comprised of a cross-section of OEM design engineers and academics — 14 professionals in total. This judging team was solely responsible for the final results.

Xilinx’s Adaptive Compute Acceleration Platform (ACAP) delivers AI inference and wireless acceleration with integrated AI engines that deliver over 100X greater compute performance than today’s server-class CPUs.

In the Embedding Computing category, Xilinx took home the gold with their Versal AI Core, the first Adaptive Compute Acceleration Platform (ACAP), is a multi-core, heterogeneous compute platform that dynamically adapts at the hardware and software level for a wide range of applications and AI workloads from edge to cloud. The platform integrates next-generation scalar engines for embedded compute, adaptable engines for hardware programmability, and intelligent AI Engines and DSP Engines for AI inference and signal processing.

The architecture is unique as it solves the most difficult problem of AI inference—compute efficiency—by coupling ASIC-class compute engines (AI engines) with adaptable hardware to implement custom memory hierarchy and custom neural network layers. Versal AI Core breaks through traditional compute bottlenecks, specialized hardware customization without manufacturing a new chip. The AI Engine delivers 8X the compute density at half the power of traditional FPGA solutions and delivers low-latency Convolutional Neural Network (CNN) execution 3.5X faster vs. traditional GPUs.

The adaptability and heterogeneous architecture of Versal AI Core is a key advantage over traditional AI accelerators that typically focus on a subset of AI workloads. AI algorithms are diverse across industries—from cloud computing to networking, to edge and autonomous system. The adaptable nature of Versal AI Core makes it applicable for a breadth of markets.

Commenting on the Versal AI Core, judges said: “A game changer for the next generation of networking electronics, medical imaging diagnostic instruments and any application needing state-of-the-art processing power.”

Picking up the silver award was Texas Instruments Jacinto 7 processor platform that brings enhanced deep learning capabilities and advanced networking to solve design challenges in ADAS and automotive gateway applications. The TDA4VM processors for ADAS and DRA829V processors for gateway systems are the first two devices in the portfolio. These devices include specialized on-chip accelerators designed to segment and expedite computer vision, deep learning and other data-intensive tasks, allowing the car to more accurately perceive and react to the world around it.

Supporting sensor fusion from cameras, LIDARs, radars and other sensors, the Jacinto 7 devices are able to accept inputs from multiple sources to not only create the most accurate vision of the world around the car, but also to utilize machine learning intelligence to help the car and driver best react to that world. Designed for L2 and L3 automated functions, these devices are targeted for applications ranging from automatic emergency braking and lane-keep assist to more accurate autonomous parking and highway autopilot.

Finally, the bronze went to Lattice Semiconductor for their CrossLink-NX FPGA, the FPGA developed on the Lattice Nexus FGPA platform, the industry’s first 28nm FD-SOI-based FPGA platform optimized for low power operation in a small form factor. The CrossLinkCrossLink-NX FPGA-NX FPGA provides the energy efficiency, small form factor, high reliability and high-performance developers in communications, compute, industrial, automotive, and consumer markets need to enable innovative embedded vision solutions for the Edge. These new chips are designed to address the latest trends in video processing: mixing multiple sensors and displays, higher resolution video, multiple interfaces, and Edge AI processing.

CrossLink-NX consumes 75 percent less power compared to competing FPGAs of a similar class. In addition, CrossLink-NX has a Soft Error Rate (SER) up to 100 times lower than similar FPGAs in its class, making it a compelling solution for mission-critical applications that must operate safely and reliably. The initial CrossLink-NX device is designed to support ruggedized environments found in outdoor, industrial, and automotive applications. CrossLink-NX delivers Instant on performance. This enables ultra-fast I/O configuration in 3 ms and total device configuration in less than 15 ms. It is available in a 6 x 6 mm form factor, which is up to ten times smaller than similar competing FPGAs in its class.

Processors target ADAS, automotive gateway applications

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Jacinto 7 processor platformTexas Instruments introduced the new Jacinto 7 processor platform. Built on the foundation of TI’s decades of automotive systems and functional safety expertise, the new Jacinto processor platform brings enhanced deep learning capabilities and advanced networking to solve design challenges in advanced driver assistance systems (ADAS) and automotive gateway applications.

New low-power, high-performance TI Jacinto 7 processors enable mass-market adoption of automotive ADAS and gateway technology.

The first two automotive devices in the platform, TDA4VM processors for ADAS and DRA829V processors for gateway systems, include specialized on-chip accelerators to segment and expedite data-intensive tasks, like computer vision and deep learning. Additionally, TDA4VM and DRA829V processors incorporate a functional safety microcontroller, making it possible for original equipment manufacturers (OEMs) and Tier-1 suppliers to support both ASIL-D safety-critical tasks and convenience features with one chip. Both devices share a single software platform, which alleviates system complexity and cost by enabling developers to reuse their software investment across multiple vehicle domains.

Through in-bound camera, radar and LIDAR data, ADAS technology helps cars see and adapt to the world around them. The influx of information coming into the car underscores the need for processors or systems-on-chip to quickly and efficiently manage multilevel processing in real time, all while operating within the system’s power budget. TI’s new processors execute high-performance ADAS operations using just 5 to 20 W of power, eliminating the need for active cooling.

The TDA4VM processor offers on-chip analytics combined with sensor pre-processing, enabling more efficient system performance. This allows OEMs and Tier-1 suppliers to support front camera applications using high-resolution 8-MP cameras to see farther and add enhanced features, such as drive assist. Additionally, TDA4VM processors are capable of simultaneously operating four to six 3MP cameras while also fusing other sensing modalities such as radar, LIDAR and ultrasonic on one chip. This multilevel capability enables TDA4VM to act as the centralized processor for ADAS and enables the critical features for automated parking, like surround view and image processing for displays, enhancing vehicle perception for 360 degrees of awareness.

The DRA829V processor seamlessly integrates the computing functions required for modern vehicles. As vehicle technology advances, automotive gateways need a flexible processor to manage higher volumes of data and support evolving requirements for autonomy and enhanced connectivity. The DRA829V processor is the first in the industry to incorporate a PCIe switch on-chip in addition to integrating an eight-port gigabit TSN-enabled Ethernet switch for faster high-performance computing functions and communications throughout the car.

Combined with the ability to support both ASIL-D safety-critical and non-safety-critical operations, these computing and communication features enable OEMs and Tier-1 suppliers to support mixed-criticality applications on one device. Higher bandwidth on-chip also allows developers to manage software development and validation in vehicles, which translates to more dynamic updates and upgrades.

Developers can get started immediately with Jacinto 7 processors development kits, and buy the new TDA4VMXEVM and DRA829VXEVM evaluation modules on TI.com for US$1,900 each. Preproduction TDA4VM and DRA8329V processors are available now, only from TI, at US$97 in 1,000-unit quantities. Volume production is expected to be available in the second half of 2020.

LIDAR and Time of Flight, Part 3: Emitters, sensors, and scanners

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LIDAR systems and ToF techniques are critical to providing self-driving cars with a detailed picture of the surrounding and is used in many research applications as well. This is part three of a four-part series on LIDAR systems and ToF techniques. 

At the front end of a LIDAR system is the photon emitter and its corresponding receiver. This part looks at these components and issues of pulse rate, power, pulse width, and more. The choice of wavelength to use is a tradeoff, as the wavelengths at which the emitters excel are not the same as those of the sensor, plus there are issues of atmospheric absorption and the solar-radiation spectrum (Figure 1).

LIDAR and Time of Flight
Fig 1: Spectral response of different emitters and photodiodes in comparison with solar spectrum. (Image: Texas Instruments)

The photon emitter

The front-end of a LiDAR system begins with the photons emitted by an electronically controllable source. In the earlier LIDAR days, the only source which had the needed characteristics was a pulsed laser diode (PLD), typically operating at 950 nm due to low atmospheric absorption. Other wavelengths were used in some installations due to issues of efficiency and other factors. Peak power of the APDs is about 50 to 100 W.

In recent years, there has been a trend to using infrared LEDs (IR LEDs) as their power output has increased to a suitable level, and their easier drive and control. Further, the IR LED as emitter is eye-safe under all conditions, with no need for Laser Class 1 safety precautions and associated regulatory concerns. LEDs also require less electrical power compared to PLDs, and the difference of a few watts is not make-or-break in the totality of an automotive LIDAR system which requires hundreds of watts and more. However, it does mean that less current has to be delivered the emitter assembly resulting in lower DC voltage drop (called IR drop, but “IR” here does not mean infrared, it means current × resistance) and the thermal dissipation is also reduced.

The photon sensor

As the reflected photons return to the LIDAR system, they must be captured and converted into an electrical signal and current. The two sensor types most often used are the conventional PIN photodiode (positive-intrinsic-negative) and the silicon avalanche photodiode (APD).

PIN diodes use the photoelectric effect to create hole-electron pairs called charge carriers which then deliver a photocurrent. (This effect is an easily observed physics and optical phenomenon which posed a major theoretical  conundrum, and which was resolved by Albert Einstein; he received the Nobel Prize for his radical explanation, not for relativity). While the PIN diode works, the conversion ratio of photons to electrons (the transfer ratio) is modest, so the sensitivity is low  and more received photons are needed for measurable current. Also, internal diode and circuit noise can obscure the generated electrons, adding to SNR weakness.

APDs expand the photoelectric principle by accelerating the charge carriers in an electric field (and so need a higher-voltage source). The use of the electric field results in additional electron-hole pairs through what is called impact ionization. This is similar to the vacuum-tube photomultiplier tube (PMT), but is smaller and with a not-as-high voltage; however, there is more noise and a lower transfer ratio compared to the PMT.

As a consequence of the APD operation, a single photon may produce hundreds or even thousands of electrons from a single photon and this higher current. A single APD may not deliver sufficient resolution and conversion gain. For this reason, APDs are often used in arrays of 8, 12, 16, or even 64 elements (Figure 2). Resolution is also determined by the pixel pitch (spacing between the APDs) which can be as tight as 40 µm.

This array combining low-noise, high-sensitivity silicon avalanche photodiodes in a monolithic assembly; the arrays have been optimized for the 800-900 nm wavelength range and include a low temperature coefficient and a gap of only 40 µm between elements. (Image: Laser Components)

The signal-conditioning electronics of the APD interface are obviously critical as well. The optimum internal gain of the transimpedance amplifier which interface with the APD is selected to match the internal noise of the APD with the input noise of the amplifier and its load resistance, so the APD does not increase system electronic noise (Figure 3). The tradeoff is that noise increases with the bandwidth of the system for both APDs and PIN diodes, so it is desirable to reduce bandwidth as much as can be tolerated without degrading system throughput.

PIN diodes and APDs are available from many vendors, including Hamamatsu, First Sensor, Osram Opto Semiconductors, and Laser Components, and a full list is much, much longer. These are highly specialized components and their suppliers range from small “boutique” sources to much larger but  still niche suppliers who focus almost exclusively on electro-optical sources and sensors and little else. This is not surprising as the world of photonics components requires very different materials, expertise, processes, and tests compared to “conventional” electronic devices based on silicon, SiC, GaAs, or other elements and compounds.

Fig 3: The interface transimpedance amplifier for the PIN diode or APD must be matched to the characteristics of the specific sensor for lowest-noise and highest-sensitivity performance. (Image source: First Sensor AG)

Scanning the LIDAR

A single pulse will just tell you what is out there in one direction (a vector) but not create an image. To do this, the electronics of many LIDAR systems use a highly refined version of an “ancient” optical fixture: a spinning-mirror scanning galvanometer (Figure 4).

Fig 4: The motor-steered, mirror-based galvanometer scanning array is a long-established, very mature, widely used arrangement for controlled a light beam‘s direction, for both emitting and sensing. (Image: m.blog.naver.com)

This is the cylindrically shaped object seen on top of many autonomous cars. The mirror assembly rotates at between one and ten revolutions per second as well as up and down. It directs the pulsed light around the 360⁰ circle and also directs the reflected bean back to the photosensor. Some mirror assemblies do not rotate around the full circle; instead, they scan back and forth over a limited angle for faster scanning.

As with any technology, there are many variations in how the actual design is implemented. Rotating LIDARs are fairly bulky and mechanical, needing special mounting to work in the harsh automotive environment. One type of solid-state LiDAR uses micro-electromechanical system (MEMS) technology with a MEMS-based based mirror moves to “scan” the environment, often based on Texas Instruments digital light projector (DLP) MEMS components. Another technique used in solid-state LIDAR is an optical phased array (OPA) with multiple independent sources that dynamically direct and target the pulses in different direction by adjusting the array timing and does not require sensor movement. OPA-based LiDAR is more durable and reliable as it does not move and it also enables fast scanning, smaller size, but price is an issue.

Unlike scanning LIDAR, flash LIDAR does not move the light or laser at all. Instead, it works like a camera, delivering a flash of light to detect the whole surrounding area at once and analyzing the information with image sensor. The light source of flash LIDAR is more powerful, but the detecting distance and field of view are much lower than with scanning LIDARs. As the pulse rate has increased while the pulse width has decreased, there are the usual issues of high-speed driver circuitry, parasitic inductance, drive-waveform fidelity, and the need to keep the driver compact and close to its load, the optical emitter.

At present, there is no non-moving, solid-state scanner which can provide 360 degrees of coverage; instead, multiple arrays with some overlap must be used. There’s some irony that the relatively “ancient” mechanical assembly is still the widely used approach, working in conjunction with advanced components and electronics such as laser diodes and high-performance graphics computing engines. Reality is that as a mature (but still evolving) device, a well-designed mechanical unit can be relatively reliable and compact, as a result of decades of experience with their implementation and likely weak points. Still, a design without moving parts is very attractive, and there are some units available.

The final part of this article looks at some hardware issues and available implementations.

EE World References

External References

 

LIDAR and Time of Flight, Part 1: Introduction

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LIDAR systems and ToF techniques are critical to providing self-driving cars with a detailed picture of the surrounding and is used in many research applications as well.

Many of the major automobile vendors, along with well-known non-auto companies such as Google, are devoting major resources to developing autonomous vehicles (often called “self-driving cars”). These vehicles obviously need to see where they are going. Doing so requires more just a photo-like flat “snapshot” of the surroundings, and even using two cameras in a “stereo” mode would also fall far short in providing the image detail, resolution, and precision needed. Instead, these vehicles need a detailed, quantifiable three-dimensional (3D) picture of their surroundings.

The solution is not to use conventional radar, optical, or ultrasound detection and ranging with a basic transmit/receive echo arrangement (recall that radar is an acronym for radio detection and ranging). Instead of RF or acoustic wavelengths with their limited resolution, LIDAR (sometimes written as lidar, or even LiDAR) uses laser- or LED-generated pulses of light. Based on extremely accurate measurements of the time for any reflections to be received, and using a technique called “time of flight” (ToF) ranging, the system can determine what’s in front of and around the vehicle.

 

LIDAR and Time of Flight
Fig 1: The smarter and eventually fully autonomous vehicle will need surrounding- and ambient-sensing and imaging based on multiple technologies. (Image: Michigan Tech Research Institute – Michigan Technological University)

 

Fig 2: The different sensing technologies complement each other, as noted by the right-most column, which merges their capabilities. (Image: ams AG/Austria Microsystems)
Fig 3: Another perspective on the relative attributes of different sensing technologies for autonomous vehicle. (Image: Michigan Tech Research Institute – Michigan Technological University)

The use of LIDAR in cars is not the complete solution to seeing what’s around the vehicle. Autonomous and even semi-autonomous ones need to combine multiple sensing technologies to do what a human driver can do (Figure 1), and the various technologies overlap and complement each other (Figure 2 and Figure 3). The technical reality is that a vehicle needs LIDAR to meet Level 3 of the six-level autonomous-driving scale (previously five-level scale ) as defined by SAE J3016 standard “Levels of Driving Automation”.

Note that LIDAR is not just for autonomous vehicles, with their relatively short-distance imaging needs and modest speeds. It was originally developed several decades ago for applications such as undersea mapping and aircraft/satellite-based geophysical mapping systems. In these cases, it is used to create high-resolution images of the Earth’s surface and structures, to measure ocean levels and waves, and even for guidance in spacecraft docking. It is also used in robotics applications, safety zone monitoring in industrial machinery, traffic monitoring, and security applications.

This article focuses on at the basics of a LIDAR system with an emphasis on automotive designs. It will also look at photon emitters and sensors for LIDAR. It will not discuss the image-processing algorithms which are so critical to making sense of the LIDAR-provided data to create an analog to the human eye, image processing, and decision-making process.

In addition, LIDAR’s adoption to mass-market designs such as cars is having the often-seen beneficial back-and-forth benefit for older, existing applications. On one side, automotive LIDAR is benefiting from the huge base of non-auto experience and understanding developed over the decades. At the same time, the mass market is driving improvements basic components ranging from optical/electronic devices to image processors and their algorithms, and many of these advances are working back into the scientific and instrumentation areas.

Part 2 of this article will look at the basic operation of a LIDAR system.

EE World References

External References

Reusable solderless robotics kit allows students to build embedded systems

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Texas Instruments introduced the newest addition to the TI Robotics System Learning Kit (TI-RSLK) family, the TI-RSLK MAX, a low-cost robotics kit and curriculum that is simple to build, code and test. Designed for the university classroom, the solderless assembly allows students to have their own fully functioning embedded system built in under 15 minutes. Classrooms that may not have access to soldering equipment benefit from the solderless, hands-on kit and curriculum that can be reused year after year.

TI launched the TI-RSLK series last year to help universities across the globe keep students engaged from their first day of class until graduation with hands-on, customizable options for learning embedded systems design. The TI-RSLK MAX completes all tasks and robotic challenges covered in the previous TI-RSLK Maze Edition kit, such as solving a maze, line following and avoiding obstacles. It also provides a user-friendly assembly of the various sub-systems, speeding up the building and testing of the robot.

The new kit includes the industry-leading SimpleLink MSP432P401R microcontroller (MCU) LaunchPad Development Kit, easy-to-connect sensors, and a versatile chassis board that turns the robot into a mobile learning platform. Through accompanying core and supplemental curriculum, students learn how to integrate their hardware and software knowledge to build and test a system. For advanced learning, wireless communication and Internet of Things (IoT) capabilities can be added to the TI-RSLK MAX to remotely control the robot or even establish robot-to robot communication.  \

“We know engineering educators are continually looking for more ways to teach complex concepts while keeping students engaged,” said Peter Balyta, Ph.D., president of TI Education Technology and vice president of academic engagement and corporate citizenship. “The TI-RSLK MAX does exactly that. Since the robotics kit can be built quickly, educators can spend more time teaching abstract and advanced concepts, while giving students a foundational hands-on understanding in real-world embedded system design.”

The TI-RSLK MAX is available for purchase for US$109 from the TI Store and includes the SimpleLink MSP432P401R MCU LaunchPad Development Kit, as well as all additional components required for assembly. To expand kit functionality and learning paths, optional accessories are available for purchase. Further information about the TI-RSLK can found at www.ti.com/rslk.

Microcontroller Top Talks in August 2019 on EDABoard.com

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Peer-to-peer, engineer-to-engineer questions and answers from the EDABoard.com engineering community around microcontrollers. Click the “Read more” link and follow the entire conversation and maybe add your two cents by logging in to EDAboard.com.

Sine PWM calculated with PIC – I have seen several posts about PWM in order to make a one phase inverter, and most use long values table calculated to get the sine wave. But what if it calculates the duty value each time?  I see I will have to compensate about 1/7 of cicle time due to the calculation time.  Read more

One audio master transmitter to multiple audio receivers – I am planning on a project where there is one audio to master transmitter to (about 8 receiver, where it receives stereo ) multiple audio receivers. How do I  lay out the project? I think Bluetooth is not enough. Read more

MOSFET gets hot in H bridge – I tried making an H bridge circuit using P Channel and N channel MOSFET and driving the MOSFETs using transistors. But the P-channel MOSFET gets extremely hot within a second. Can anybody please let me know where the problem is? Read more


Interfacing IR remote using NEC protocol – I am trying to interface IR remote control (NEC Protocol) using PIC16f887p microcontroller. The coding objective is to read the IR signal and has to display the equivalent HEX value of command and address. While testing in hardware, the IR signal is not detecting but got random output. Read more

How to stop micro burning when GND & VCC short circuit –  If I short circuit VCC and GND, the micro burns. I am using LM2596 3.3V. The LM2596 schematic is below. How can I protect the micro from this? Read more

STM32F3 built-in operation amplifier – I look into datasheet STM32F303 page 122 6.3.21 operational amplifier characteristics, and I’m a little confused. Why does ST not specify typical voltage offset of inputs? Why does ST not specify typical input current of inputs? Read more

Loop termination with data in array is 0ff – I’m working on a project which is to send a binary file to EEPROM, read it back and display it on UART terminal. I’m using PIC16F877A microcontroller, xc8 compiler, and 93lc46a EEPROM. I’m able to sending/reading the file to/from EEPROM properly. I split the binary file (size 128 bytes) into two arrays. The code here is for first 64 bytes of data. In the code, I’m comparing each byte of data. The problem is the for loop terminates at j = 62, (RxData[62] = FF). I don’t know why. Read more
Zero crossing detecting formula 3-phase network – I am looking for the simple 3 phase zero-crossing ckt and its formula for calculating it. I am trying with Arduino Uno board. Suggestions? Read more
Is this a good circuit? – I am concerned about RN8 (24R). It allows a max common anode current of 500 mA per digit and also a max 500mA can sink through each channel of the ULC2823. Read more
Enc28j60 with STM32F4 – How can we can link our own monitoring website with Ethernet controller interfacing with STM32f4 project? I want to monitor the LED through a web page on the browser by inserting an IP address like 198.168.1.119/page and done by me on localhost (local network). Read more

MCUs integrate, CAN FD, Ethernet, EtherCAT industrial communication protocols

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C2000 F2838xTexas Instruments introduced new communications capabilities on its C2000 microcontrollers (MCUs). C2000 F2838x 32-bit MCUs enable designers to use a single chip to implement connectivity, including EtherCAT, Ethernet and Controller Area Network with Flexible Data Rate (CAN FD), in AC servo drives and other industrial systems.

Systems with communication interfaces often require an external application-specific integrated circuit (ASIC) or dedicated host control microprocessor, which limits the flexibility of the design architecture, adds complexity and takes up space on the board. The new C2000 F2838x MCUs do not require an external ASIC, therefore reducing overall solution size and bill of materials.

By integrating three industrial communication protocols, the F2838x MCUs give designers the ability to tailor one MCU to the unique needs of each system. A key component in achieving this is a new connectivity manager, an Arm Cortex-M4-based subsystem, which offloads processing-intensive communications and optimizes connectivity. In addition to these capabilities, C2000 F2838x MCUs offer enhanced real-time control performance and higher flexibility than previous C2000 series MCUs.

Key features 

  • Integrates EtherCAT, Ethernet and CAN FD: For electrically isolated architectures, the new MCUs use a fast serial interface with eight receiving channels to facilitate chip-to-chip communication at up to 200 Mbps using minimal pins. Designers can build on this high level of integration in CAN FD designs and quickly increase the number of available CAN FD ports by pairing the F2838x with a TI system basis chip (SBC), such as the TCAN4550 SBC with integrated CAN FD controller and transceiver.
  • Maximizes real-time control performance: With a 64-bit floating-point unit and fast integer division hardware, the C28x central processing unit-based control allows for differentiated capability and high-precision designs. For servo drive applications, the fast current-loop technology processes field-oriented control in less than 500 ns to enable more precise position control.
  • Flexible integration of sensing facilitates real-time control: The C2000 F2838x series also integrates a single-ended 16-bit analog-to-digital converter, which doubles the number of available channels compared to previous C2000 MCUs to minimize external components, minimize system latency and maximize control-loop accuracy. An expanded configurable logic block enables designers to customize peripherals and removes or reduces the need for a field-programmable gate array.

Pre-production samples of the C2000 TMS320F28388D are now available through the TI store in a 337 ball-grid-array package. Pricing starts at US$14.00 in 1,000-unit quantities. Developers can get started with the C2000 TMDSCNCD28388D development kit, available through the TI store for $249. Software development kits for motor control and digital power will be released by the end of June.

Automotive SBC helps designers add CAN FD without changing their microcontroller

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TCAN4550-Q1Texas Instruments introduced an automotive system basis chip that is the industry’s first to integrate a controller and transceiver for Controller Area Network with Flexible Data Rate (CAN FD). Designed to meet the high-bandwidth and data-rate flexibility needs of in-vehicle networks, the TCAN4550-Q1 uses the Serial Peripheral Interface (SPI) bus of almost any microcontroller to implement, with minimal hardware changes, a CAN FD interface or increase the amount of CAN FD bus ports in a system.

Previously, designers had to incorporate multiple discrete components into their designs or change microcontrollers entirely when upgrading to or expanding CAN FD functionality – an often time-consuming and expensive process. With the TCAN4550-Q1 system basis chip (SBC), designers can maintain their existing microcontroller-based architecture and streamline CAN FD upgrade or expansion in body electronics and lighting, advanced driver assistance systems (ADAS) and automotive gateway designs.

The CAN FD communications protocol builds on the original CAN bus standard (also known as “Classical CAN”) to help ensure that automotive microcontrollers and connected systems can communicate effectively as the variety and speed of data rates throughout in-vehicle networks continue to evolve. With support for data rates up to 5 Mbps and payloads up to 64 bytes, the CAN FD protocol empowers designers to move data faster in their next-generation automotive applications.

Key features

  • Reduced bill of materials (BOM) and system costs: Designers can simplify their designs with the high integration of the TCAN4550-Q1 – including integrated ±58-VDC bus fault protection, watchdog timer and fail-safe modes – and its cross-compatibility with the Classical CAN protocol.
  • Easier bus expansion in automotive designs: Designers can also use the chip to add more CAN FD buses via the existing SPI port in an automotive system when the microcontroller has a limited number of CAN FD ports. Typically this type of bus expansion can require system redesign, which is not necessary when using the TCAN4550-Q1.
  • Small power design footprint: With an integrated 125-mA low-dropout (LDO) linear regulator, the TCAN4550-Q1 can power itself while also providing a 70-mA external output that can be used to power sensors or additional components, which enables a smaller power design footprint by reducing the need for external power components.
  • Reduced power consumption: The TCAN4550-Q1 helps designers lower system power consumption when in standby mode through wake and inhibit features.
  • Increased maximum data rate: The new SBC enables faster programmability of automotive software during vehicle assembly through a maximum data rate of 8 Mbps, which exceeds the 5-Mbps maximum data rate of the CAN FD protocol.

This new device joins TI’s growing portfolio of SBCs that integrate CAN FD, CAN, and Local Interconnect Network (LIN) transceivers and power components into single-chip solutions, including the TCAN4550-Q1 and TLIN1441-Q1. These highly integrated devices help designers reduce their BOM, increase system performance and get their designs to market faster, as compared to competing devices with fewer integrated components. Designers can also pair TI SBCs with TI C2000 microcontrollers to quickly and easily expand the amount of available bus ports while increasing overall system functionality.

The TCAN4550-Q1 is available now through the TI store and authorized distributors. The TCAN4550-Q1 comes in a 4.5-mm-by-3.5-mm very thin quad flat no-lead package and is priced at US$1.79 in 1,000-unit quantities.

Fast 12-bit ADC features 8-GHz bandwidth, 10.4-GS/sec sampling rate

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ADC12DJ5200RFTexas Instruments today introduced a new ultra-high-speed analog-to-digital converter (ADC) with the industry’s widest bandwidth, fastest sampling rate and lowest power consumption. The ADC12DJ5200RF helps engineers achieve high measurement accuracy for 5G testing applications and oscilloscopes, and direct X-band sampling for emerging technologies in radar, microwave, and mmWave.

“In general, what we are doing is pushing the envelope of what is out there,” says Phillip Pratt, TI’s Business Development and Technical Marketing Manager for High Speed Data Converters, “The 10 mm x 10 mm footprint of the device integrates an entire RF-to-bits receiver to drive out to the 9 – 10 GHz frequencies. Removing the RF stages, as well as integrating the DDC (digital down converter) means we can decrease the number of components needed to downconvert. Additionally, JESD204C standard interface support means fewer SERDES connections are needed to out data to FPGAs.”

At 8 GHz, the ADC12DJ5200RF enables engineers to achieve as much as 20 percent higher analog input bandwidth than competing devices, which gives engineers the ability to directly digitize very high frequencies without the power consumption, cost and size of additional down-conversion.

In dual-channel mode, the ADC12DJ5200RF samples at 5.2 gigasamples per second (GSPS) and captures instantaneous bandwidth (IBW) as high as 2.6 GHz at 12-bit resolution. In single-channel mode, the new ultra-high-speed ADC samples at 10.4 GSPS and captures IBW up to 5.2 GHz.

The ADC has the highest available dynamic performance across power-supply variations, even at minimum specifications, which improves signal intelligence by providing ultra-high receiver sensitivity to detect even the smallest and weakest signals. In addition, the device includes internal dither which improves spurious-free performance. System errors are minimized with offset error as low as ±300 µV and zero temperature drift.

Engineers designing test and measurement equipment can achieve high measurement repeatability by taking advantage of the extremely low code error rate (CER) of the ADC12DJ5200RF, which is more than 100 times better than competing devices. At 4-W power consumption, the device achieves 20 percent less power consumption than competitive ADCs

The ADC12DJ5200RF is pin-compatible with the following other TI GSPS ADCs to provide an easy upgrade path from 2.7 GSPS to 10.4 GSPS, and minimizes the time and cost of redesign: ADC12DJ3200, ADC12DJ2700 and ADC08DJ3200.

The ADC12DJ5200RFEVM and TSW14J57EVM evaluation modules are available today from the TI store and authorized distributors.

The ADC12DJ5200RF dual- and single-channel ultra-high-speed ADC is available for sampling through the TI store. The device is in a 144-ball, 10-by-10-mm flip-chip ball grid array (FCBGA) package.

Key factors to consider when choosing a microcontroller

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When designing an embedded system with an MCU, which one should you pick? This guide provides you with a quick overview to help you narrow down your choices.

A microcontroller (MCU), is the smallest and most basic computer. Running on a single chip, it has a CPU, memories (RAM and ROM), and interfaces (input/output ports) for additions like video, audio, USB, and cameras. Unlike a microprocessor, which serves more generalized applications, a microcontroller aims at more specific applications.

A microcontroller has many uses: in consumer electronics like toys and appliances, in communication devices like cell phones and computers, and in medical instruments like ECG machines and monitors. Also, in industrial settings, it can be used to control temperature or pressure, count time, measure speed, or activate the braking system. Because microcontrollers are suitable for specific tasks, it is essential to choose a microcontroller that is most appropriate for a project. There are many factors to consider.

Power efficiency.  There is a trade-off between processing performance and power consumption: a device with higher processing power will consume more energy.  Therefore, if your microcontroller is wireless and running on a rechargeable battery, you need to weigh sacrificing power efficiency against getting more processing power, or vice versa. Some power-efficient microcontrollers include STMicroelectronics’ STM32 series, NXP’s Kinetis L series, Renesas’ H8 Super Low Power series, Cypress’ PSoC 6 series, Microchip’s eXtreme Low Power PIC series, and Texas Instruments’ MSP430 series.

Temperature tolerance.  Depending on the environment in which your microcontrollers operate, you may want devices that withstand extreme temperature. There will be a trade-off between temperature tolerance and cost. Some temperature-tolerant microcontrollers include STMicroelectronics’ STM32F103 series, NXP’s Kinetis EA series, Renesas’ RX24T and RX24U, Infineon’s XMC series and AURIX™ series, Microchip’s: PIC and AVR microcontrollers, and Texas Instruments’ MSP430F2619S-HT.

Security.  Hacking which targets  IoT devices is rising, a threat that is especially relevant to microcontrollers used in automobiles.  In response, microcontroller makers are implementing layers of security such as cryptography and physical security. Now, users can purchase microcontrollers that have been certified to the latest security standards or use MCUs with on-chip secure hardware.  Companies that offer stand-alone security MCUs include STMicroelectronics (ST33 series), Renesas (AE-5 and RS-4 series), Infineon (OPTIGA Trust and OPTIGA TPM), Cypress (PSoC 64), Microchip (32-bit and 16-bit MCUs for Security), and Texas Instruments (MSP430 series).  Companies that offer on-chip security hardware include NXP (Kinetis series) and Infineon (AURIX).

Hardware architecture. A microcontroller’s packaging directly influences its size and performance.  Dual in-line packaging is the most common type. Small-outline transistors have a small footprint, and quad flat packs take up more areas but less vertical space.  Wafer level chip-scales are much smaller and pack in more processing power but are more expensive to manufacture.  Flat no-lead packages are better in heat diffusion. Ball grid arrays (BGAs) have high performance due to the compact package but also cost more to fabricate.

Some of the smallest footprint microcontrollers include Microchip’s (formerly Atmel’s) ATtiny20-UUR, Cypress’ PSoC 4000, NXP’s LPC1102UK, STMicroelectronics’ STM32F042T6Y6, and Texas Instrument’s MSP430G2252.

Processing power.  How much processing power do you require for the task, will a single core processor suffice, or do you need a dual-core? A multicore processor will be significantly faster, but it will also consume more energy. Also, will a graphics processing unit (GPU) be necessary?

Memory. The amount of memory (RAM and ROM) you need will depend on the programs you will be running. More programs need more random access memory (RAM). In addition, a GPU will require not only more RAM but faster read/write time as well.

Hardware interface. The nature of the task will dictate the need for hardware interfaces such as USB, Wi-Fi, Bluetooth, audio, video, or camera.

Software architecture.  Some microcontrollers are operable on multiple OSs, and others are not. If you need to scale, it is better to use the same software architecture to increase interoperability.

Cost.  Microcontrollers fall within a wide price range, from a hundred units for a few dollars to a few dollars per unit. If you want to scale, you need to consider the overall cost versus the individual performance power of a microcontroller.

Here are examples of specialty microcontrollers from different manufacturers:

Company Power Efficiency Temperature Tolerance Security Small Size
STMicroelectronics STM32 series STM32F103 series ST33 MCUs STM32F042T6Y6
NXP Kinetis L series Kinetis EA series Kinetis LPC1102UK
Renesas H8 Super Low Power series RX24T and RX24U AE-5 and RS-4 
Infineon XMC series and AURIX series OPTIGA™ Trust, OPTIGA™ TPM, and AURIX™
Cypress PSoC 6® series PSoC® 64 PSoC 4000
Microchip eXtreme Low Power PIC® series PIC® and AVR® 32-bit and 16-bit MCUs ATTiny20-UUR
Texas Instruments MSP430 series MSP430F2619S-HT MSP430 MCUs MSP430G2252