NXP’s Latest i.MX Applications Processors for IoT/Wearables and Automotive – Here’s Why They’re on FD-SOI [Part 1 of 2]

By Ronald M. Martino, Vice President, i.MX Applications Processor and Advanced Technology Adoption, NXP Semiconductors

The latest generations of power efficient and full-featured applications processors in NXP’s very successful and broadly deployed i.MX platform are being manufactured on 28nm FD-SOI. The new i.MX 7 series leverages the 32-bit ARM v7-A core, targeting the general embedded, e-reader, medical, wearable and IoT markets, where power efficiency is paramount. The i.MX 8 series leverages the 64-bit ARM v8-A series, targeting automotive applications, especially driver information systems, as well as high-performance general embedded and advanced graphics applications.

Over 200 million i.MX SOCs have been shipped over six product generations since the i.MX line was first launched (by Freescale) in 2001. They’re in over 35 million vehicles today, are leaders in e-readers and pervasive in the general embedded space. But the landscape for the markets targeted by the i.MX 7 and i.MX 8 product lines are changing radically. While performance needs to be high, the real name of the game is power efficiency.

Why are we moving to FD-SOI?

The bottom line in chip manufacturing is always cost. A move from 28nm HKMG to 14nm FinFET would entail up to a 50% cost increase. Would it be worth it? While FinFETs do boast impressive power-performance figures, for applications processors targeting IoT, embedded and automotive, we need to look beyond those figures, taking into account:

  • when and how performance is needed and how it is used;
  • when power savings are most pertinent;
  • how RF and analog characteristics are integrated;
  • the environmental conditions under which the chip will be operating;
  • and of course the overall manufacturing risks.

In fact, both NXP and the former Freescale have extremely deep SOI expertise. Freescale developed over 20 processors based on partially-depleted SOI over the last decade; and NXP, having pioneered SOI technology for high-voltage applications, has dozens of SOI-based product lines. So we all understand how SOI can help us strategically leverage power and performance. For us, FD-SOI is just the latest SOI technology, this time with a design flow almost identical to bulk, but on ultra-thin SOI wafers and some important additional perks like back-biasing.

When all the factors we care about for the new i.MX processor families are tallied up, FD-SOI comes out a clear winner for i.MX SOCs.

FD-SOI: Designing for Power, Performance and more

For our designers, here’s why FD-SOI is the right solution to the engineering challenges they faced in meeting evolving market needs.

In terms of power, you can lower the supply voltage (Vdd) – so you’re pulling less power from your energy source – and still get excellent performance. Add to that the dynamic back-biasing techniques (forward back-bias improves performance, while reverse back-bias reduces leakage) available with FD-SOI (but not with FinFETs), you get a very large dynamic operating range.FDSOIslideadvtg1NXP

By dramatically reducing leakage, reverse back-biasing (RBB) gives you good power-performance at very low voltages and a wide range of temperatures. This is particularly important for IoT products, which will spend most of their time in very low-power standby mode followed by short bursts of performance-intense activity. We can meet the requirements for those high-performance instances with forward back-biasing (FBB) techniques. And because we can apply back-biasing dynamically, we can specify it to meet changing workload requirements on the fly. [Editor’s note: click here and here for helpful ASN articles with descriptions and discussions of back-biasing, which is also sometimes called body-biasing.]

Devices for IoT also have major analog and RF elements, which do not scale nearly so well as the digital parts of the chip. Furthermore analog and RF elements are very sensitive to voltage variations. It is important that the RF and analog blocks of the chip are not affected by the digital parts of a chip, which undergo strong, sudden signal switching. The major concerns for our analog/RF designers include gain, matching, variability, noise, power dissipation, and resistance. Traditionally they’ve used specialized techniques, but FD-SOI makes their job much easier and results in superior analog performance.

In terms of RF, FD-SOI greatly simplifies the integration of RF blocks for WiFi, Bluetooth or Zigbee, for example, into an SOC.

Soft error rates (SER)* are another important consideration, especially as the size and density of SOC memory arrays keep increasing. Bulk technology gets worse SER results with each technology node, while FD-SOI provides ever better SER reliability with each geometry shrink. In fact, 28nm FD-SOI provides 10 to 100 times better immunity to soft-errors than its bulk counterpart.

Our process development strategy has always been to leverage foundry standard technology and adapt it for our targeted applications, with a focus on differentiating technologies for performance and features. We typically reuse about 80% of our technology platform, and own our intellectual property (IP). Looking at the ease of porting existing platform technology and IP, and analyzing die size vs. die cost, again, FD-SOI came out the clear choice.FDSOIslide2costNXP

In terms of manufacturing, FD-SOI is a lower-risk solution. Integration is simpler, and turnaround time (TAT) is much faster. 28nm FD-SOI is a planar technology, so it’s lower complexity and extends our 28nm installed expertise base. Throughout the design cycle, we’ve worked closely with our foundry partner, Samsung. They provided outstanding support, and very quickly reached excellent yield levels, which is of course paramount for the rapid ramp we anticipate on these products.

In the second part of this article, we’ll take a look at the new i.MX product lines, and why FD-SOI is helping us make those game-changing plays for specific markets.

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* Soft errors occur when alpha or neutron particles hit memory cells and change their state, giving an incorrect read. These particles can either come from cosmic rays, or when radioactive atoms are released into the chips as materials decay.

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