Small, Sixth-Generation Silicon TV Tuners

Silicon Labs recently launched the sixth generation of its high-performance TV tuner ICs. The Si2151 and Si2141 TV tuners are intended for the global hybrid TV and digital TV markets. Both support digital and analog video broadcasts, and they comply with all worldwide terrestrial/cable TV standards.SiliconLabs-TV-Tuner

  • The Si2151/41 tuners fully comply with the China GB/T 26686-2011 general specification for digital terrestrial television receivers.
  •  The Si2151/41 tuners deliver a large margin to this specification for the VHF-Low, VHF-High and UHF frequency bands.
  • At just 3 mm x 3 mm QFN, the Si2151/41 devices are the smallest TV tuner ICs available today.
  • The Si2151/41 devices require no balun on the RF input, and they integrate all tracking filter inductors, which dramatically reduces system cost and complexity.
  • The Si2151/41 tuners require no external power transistor for single-supply operation and eliminate the need for external inductive power supply filtering, resulting in the most cost-effective, highest performance on-board TV tuner designs
  • The Si2151/41 family shares a common API with Silicon Labs’ entire TV tuner portfolio.

Samples and production quantities of the Si2151/41 TV tuners are available now in a 3 mm x 3 mm 24-QFN package. The Si2151 worldwide hybrid TV tuner costs $0.72 in 10,000-unit quantities. The Si2141 worldwide digital TV tuner is priced at $0.70 in 10,000-unit quantities. Si2151-A-EVB and the Si2141-A-EVB evaluation boards are also available for $395.

Source: Silicon Labs


Long-Range, Memory Jewelry-Tagging Solution

EMThe EM4126 EPC radio-frequency identification (RFID) IC is designed to provide RFID tagging on small and/or high-value products (e.g., jewelry and watches). The IC’s high sensitivity enables long read ranges. EM4126-based tags can achieve –21-dBm read sensitivities. The ICs are designed for supply chain management, tracking and tracing, container identification, and access and asset control applications.

The EM4126’s 224 bits of nonvolatile memory support International Organization for Standardization (ISO) or Electronic Product Code (EPC) data structures and enable SGTIN-198 encoding, which uses alphanumeric serialization represented as a string of up to 20 7-bit characters. The EM4126’s additional features include ISO 18000-63 and EPC Class-1 Generation-2 compliance, 32-bit short-tag identification, 40-to-160 Kbps forward- and return-link data rates, and a –40°C-to-85°C extended temperature range.

Contact EM Microelectronic for pricing.

EM Microelectronic

Battery Charger Design (EE Tip #130)

It’s easy to design a good, inexpensive charger. There is no justification for selling cheap, inadequate contraptions. Many companies (e.g., Linear Technology, Maxim, Semtech, and Texas Instruments) supply inexpensive battery management ICs. With a few external parts, you can build a perfect charger for just about any battery.

Texas Instruments’s UC2906 is an older (Unitrode) IC designed to build an excellent sealed lead-acid battery charger with a sophisticated charging profile. Figure 1 shows the recommended charger circuit.

Figure 1: This lead-acid battery charger uses Texas Instruments’s UC2906 IC.

Figure 1: This lead-acid battery charger uses Texas Instruments’s UC2906 IC.

In addition to the IC, only a handful of resistors and a PNP power transistor Q1 are needed to build it. Q1 must be rated for the maximum charging current and fitted with a heatsink.

An LED with its current-limiting resistor R can be connected to pin 7, which is an open-collector NPN transistor, to indicate the presence of power. Similarly, an LED with a series resistor could be connected to pin 9, which is also an open-collector NPN transistor to indicate overcharge (it is not used in Figure 1). The UC2906 datasheet and the Application Note provide tables and equations for selection of resistors Rs, Rt, RA, RB, RC, and RD and suggestions for adding various features.

Editor’s Note: This is an excerpt from an article written by George Novacek, “Battery Basics (Part 3): Battery Management ICs,” Circuit Cellar 280, 2013.

“No Opto” Synchronous Forward Controller

LinearThe LT3752/LT3752-1 is a high-input voltage-capable synchronous forward controller with an active clamp transformer reset. A controlled VOUT start-up and shut-down is maintained with an integrated housekeeping controller to bias the primary and secondary ICs. The internal bias generation also reduces the main power transformer’s complexity and size by avoiding the need for extra windings to create bias supplies.

The LT3752 operates over a 6.5-to-100-V input voltage range. The LT3752-1 is well suited for hybrid vehicle (HV) and hybrid electric vehicle (HEV) applications. For up to 400-V inputs and greater, it enables RC start-up from the input voltage with the maximum voltage limited only by the choice of external components.

A ±5% output voltage regulation can be attained without using an optocoupler. An optocoupler can be used to obtain ±1.5% output voltage regulation. The LT3752/-1 uses a pulse transformer to send a control signal to a secondary-side MOSFET driver for the synchronous rectification timing. It can also be used in self-driven applications the power transformer pulses control the secondary-side MOSFETs. With the LT3752/-1, secondary-side ICs no longer require start-up circuitry to operate when the output voltage is 0 V, which enables a controlled VOUT start-up.

The LT3752/-1 has a programmable 100-to-500-kHz operating switching frequency. It can be synchronized to an external clock, so a range of output inductor values and transformer sizes can be used.

The LT3752/-1 is available in a TSSOP-38 package with several pins removed for high-voltage spacing. The LT3752/-1 E- and I-grade versions function from a –40°C-to-125°C junction temperature. The LT3752/-1 H-grade functions from a –40°C-to-150°C operating junction temperature. The LT3752/-1 MP-grade functions from –55°C-to-150°C operating junction temperature.

The LT3752/LT3752-1 costs $3.39 in 1,000-piece quantities.

Linear Technology Corp.

Q&A: Marilyn Wolf, Embedded Computing Expert

Marilyn Wolf has created embedded computing techniques, co-founded two companies, and received several Institute of Electrical and Electronics Engineers (IEEE) distinctions. She is currently teaching at Georgia Institute of Technology’s School of Electrical and Computer Engineering and researching smart-energy grids.—Nan Price, Associate Editor

NAN: Do you remember your first computer engineering project?

MARILYN: My dad is an inventor. One of his stories was about using copper sewer pipe as a drum memory. In elementary school, my friend and I tried to build a computer and bought a PCB fabrication kit from RadioShack. We carefully made the switch features using masking tape and etched the board. Then we tried to solder it and found that our patterning technology outpaced our soldering technology.

NAN: You have developed many embedded computing techniques—from hardware/software co-design algorithms and real-time scheduling algorithms to distributed smart cameras and code compression. Can you provide some information about these techniques?

Marilyn Wolf

Marilyn Wolf

MARILYN: I was inspired to work on co-design by my boss at Bell Labs, Al Dunlop. I was working on very-large-scale integration (VLSI) CAD at the time and he brought in someone who designed consumer telephones. Those designers didn’t care a bit about our fancy VLSI because it was too expensive. They wanted help designing software for microprocessors.

Microprocessors in the 1980s were pretty small, so I started on simple problems, such as partitioning a specification into software plus a hardware accelerator. Around the turn of the millennium, we started to see some very powerful processors (e.g., the Philips Trimedia). I decided to pick up on one of my earliest interests, photography, and look at smart cameras for real-time computer vision.

That work eventually led us to form Verificon, which developed smart camera systems. We closed the company because the market for surveillance systems is very competitive.
We have started a new company, SVT Analytics, to pursue customer analytics for retail using smart camera technologies. I also continued to look at methodologies and tools for bigger software systems, yet another interest I inherited from my dad.

NAN: Tell us a little more about SVT Analytics. What services does the company provide and how does it utilize smart-camera technology?

MARILYN: We started SVT Analytics to develop customer analytics for software. Our goal is to do for bricks-and-mortar retailers what web retailers can do to learn about their customers.

On the web, retailers can track the pages customers visit, how long they stay at a page, what page they visit next, and all sorts of other statistics. Retailers use that information to suggest other things to buy, for example.

Bricks-and-mortar stores know what sells but they don’t know why. Using computer vision, we can determine how long people stay in a particular area of the store, where they came from, where they go to, or whether employees are interacting with customers.

Our experience with embedded computer vision helps us develop algorithms that are accurate but also run on inexpensive platforms. Bad data leads to bad decisions, but these systems need to be inexpensive enough to be sprinkled all around the store so they can capture a lot of data.

NAN: Can you provide a more detailed overview of the impact of IC technology on surveillance in recent years? What do you see as the most active areas for research and advancements in this field?

MARILYN: Moore’s law has advanced to the point that we can provide a huge amount of computational power on a single chip. We explored two different architectures: an FPGA accelerator with a CPU and a programmable video processor.

We were able to provide highly accurate computer vision on inexpensive platforms, about $500 per channel. Even so, we had to design our algorithms very carefully to make the best use of the compute horsepower available to us.

Computer vision can soak up as much computation as you can throw at it. Over the years, we have developed some secret sauce for reducing computational cost while maintaining sufficient accuracy.

NAN: You wrote several books, including Computers as Components: Principles of Embedded Computing System Design and Embedded Software Design and Programming of Multiprocessor System-on-Chip: Simulink and System C Case Studies. What can readers expect to gain from reading your books?

MARILYN: Computers as Components is an undergraduate text. I tried to hit the fundamentals (e.g., real-time scheduling theory, software performance analysis, and low-power computing) but wrap around real-world examples and systems.

Embedded Software Design is a research monograph that primarily came out of Katalin Popovici’s work in Ahmed Jerraya’s group. Ahmed is an old friend and collaborator.

NAN: When did you transition from engineering to teaching? What prompted this change?

MARILYN: Actually, being a professor and teaching in a classroom have surprisingly little to do with each other. I spend a lot of time funding research, writing proposals, and dealing with students.

I spent five years at Bell Labs before moving to Princeton, NJ. I thought moving to a new environment would challenge me, which is always good. And although we were very well supported at Bell Labs, ultimately we had only one customer for our ideas. At a university, you can shop around to find someone interested in what you want to do.

NAN: How long have you been at Georgia Institute of Technology’s School of Electrical and Computer Engineering? What courses do you currently teach and what do you enjoy most about instructing?

MARILYN: I recently designed a new course, Physics of Computing, which is a very different take on an introduction to computer engineering. Instead of directly focusing on logic design and computer organization, we discuss the physical basis of delay and energy consumption.

You can talk about an amazingly large number of problems involving just inverters and RC circuits. We relate these basic physical phenomena to systems. For example, we figure out why dynamic RAM (DRAM) gets bigger but not faster, then see how that has driven computer architecture as DRAM has hit the memory wall.

NAN: As an engineering professor, you have some insight into what excites future engineers. With respect to electrical engineering and embedded design/programming, what are some “hot topics” your students are currently attracted to?

MARILYN: Embedded software—real-time, low-power—is everywhere. The more general term today is “cyber-physical systems,” which are systems that interact with the physical world. I am moving slowly into control-oriented software from signal/image processing. Closing the loop in a control system makes things very interesting.

My Georgia Tech colleague Eric Feron and I have a small project on jet engine control. His engine test room has a 6” thick blast window. You don’t get much more exciting than that.

NAN: That does sound exciting. Tell us more about the project and what you are exploring with it in terms of embedded software and closed-loop control systems.

MARILYN: Jet engine designers are under the same pressures now that have faced car engine designers for years: better fuel efficiency, lower emissions, lower maintenance cost, and lower noise. In the car world, CPU-based engine controllers were the critical factor that enabled car manufacturers to simultaneously improve fuel efficiency and reduce emissions.

Jet engines need to incorporate more sensors and more computers to use those sensors to crunch the data in real time and figure out how to control the engine. Jet engine designers are also looking at more complex engine designs with more flaps and controls to make the best use of that sensor data.

One challenge of jet engines is the high temperatures. Jet engines are so hot that some parts of the engine would melt without careful design. We need to provide more computational power while living with the restrictions of high-temperature electronics.

NAN: Your research interests include embedded computing, smart devices, VLSI systems, and biochips. What types of projects are you currently working on?

MARILYN: I’m working on with Santiago Grivalga of Georgia Tech on smart-energy grids, which are really huge systems that would span entire countries or continents. I continue to work on VLSI-related topics, such as the work on error-aware computing that I pursued with Saibal Mukopodhyay.

I also work with my friend Shuvra Bhattacharyya on architectures for signal-processing systems. As for more unusual things, I’m working on a medical device project that is at the early stages, so I can’t say too much specifically about it.

NAN: Can you provide more specifics about your research into smart energy grids?

MARILYN: Smart-energy grids are also driven by the push for greater efficiency. In addition, renewable energy sources have different characteristics than traditional coal-fired generators. For example, because winds are so variable, the energy produced by wind generators can quickly change.

The uses of electricity are also more complex, and we see increasing opportunities to shift demand to level out generation needs. For example, electric cars need to be recharged, but that can happen during off-peak hours. But energy systems are huge. A single grid covers the eastern US from Florida to Minnesota.

To make all these improvements requires sophisticated software and careful design to ensure that the grid is highly reliable. Smart-energy grids are a prime example of Internet-based control.

We have so many devices on the grid that need to coordinate that the Internet is the only way to connect them. But the Internet isn’t very good at real-time control, so we have to be careful.

We also have to worry about security Internet-enabled devices enable smart grid operations but they also provide opportunities for tampering.

NAN: You’ve earned several distinctions. You were the recipient of the Institute of Electrical and Electronics Engineers (IEEE) Circuits and Systems Society Education Award and the IEEE Computer Society Golden Core Award. Tell us about these experiences.

MARILYN: These awards are presented at conferences. The presentation is a very warm, happy experience. Everyone is happy. These things are time to celebrate the field and the many friends I’ve made through my work.