Semiconductor Engineering sat down to debate the affect of superior node chips and superior packaging on automotive reliability with Jay Rathert, senior director of strategic collaborations at KLA; Dennis Ciplickas, vp of superior options at PDF Options; Uzi Baruch, vp and normal supervisor of the automotive enterprise unit at OptimalPlus; Gal Carmel, normal supervisor of proteanTecs‘ Automotive Division; Andre van de Geijn, enterprise growth supervisor at yieldHUB; and Jeff Phillips, go to market lead for transportation at Nationwide Devices. What follows are excerpts of that dialog. To view half certainly one of this dialogue, click on right here. Half two is right here.

SE: At 5nm, which is the place a few of the automotive AI chips are being developed, we’ve received course of variation, electromigration, electromagnetic interference, energy supply points and inspection challenges, amongst different issues. And we’ve by no means put an advanced-node chip into an excessive surroundings previously. Do we actually perceive what’s forward and the best way to cope with it?

Phillips: We all know there’s going to be quite a lot of change within the necessities, the use instances, the expectations, and the requirements round autonomous driving, together with how autos react and the kinds of choices they will make and never make when a human life is on the road. Finally, it’s going to be us determining the best way to consolidate and tie these issues collectively. That will probably be essential for us to even have the ability to adapt from the manufacturing and verification of the habits of the chip. On high of that, we have to put in place the suitable habits and autonomy, having algorithms on there so the automotive could make the suitable choices. Knowledge is the important thing to that.

SE: We even have software program coming into this image. If you happen to replace one a part of a fancy system, you’re probably affecting every part in that system. And should you add numerous software program, efficiency degrades, and that may affect each automotive on the street.

Ciplickas: Software program is difficult as a result of it doesn’t comply with any guidelines of physics. {Hardware} sounds arduous, nevertheless it truly follows some boundary circumstances. With software program, you possibly can change one factor that may have large unintended penalties.

Carmel: Utilizing deep knowledge, we virtualize the {hardware} to higher sense the affect of software program operations. With these virtualizations, you possibly can shift to an adaptive software program mannequin that’s tailor-made to the automobile’s ECU efficiency and in-field degradation. The AI utility will increase the portion of AI on the chip to satisfy the software program’s demand. This suggestions will assist to scale back redundancies and make sure that features are optimized. As well as, in-field inferencing and coaching will repeatedly enhance how {hardware} and software program work together with each other.

Ciplickas: We’ve talked about 5nm chips as an entire new world the place we’ve by no means been earlier than, and the challenges of taking all that knowledge and assimilating and connecting all of it. The important thing within the superior applied sciences is definitely to know what knowledge you’re lacking. For instance, the Center-of-Line (MOL) in 5nm has three-dimensional electrical interactions that you just can’t see with a bodily inspection. It is a main motive we’ve been pursuing inline ‘Design-for-Inspection’ — to get a delicate measure of leakage, which in flip signifies latent defects that threat turning into actual defects. To correctly reply, it’s a must to know that the defects are there within the first place, which suggests it’s a must to create new knowledge. Merely taking the information that’s offered as an artifact of the manufacturing course of is just not enough. Differentiated knowledge is required.

SE: What has to vary in each inspection and metrology to have the ability to establish these issues? And what has to vary from a take a look at perspective to know what’s happening right here?

Rathert: The largest problem is just not seeing the defects per se, however understanding which of them are going to be related — which of them may turn into an activated latent defect. What I might like to see, and it doesn’t exist right now, is a few connection to the designer’s thoughts that claims, ‘These are my vital areas for reliability,’ and a few connection to the take a look at engineer’s thoughts that claims, ‘These parts are troublesome to check.’ Then I might enhance my worth proposition by having the ability to focus inspection there and report knowledge that’s remoted to these areas, and feed that again to harden designs and enhance take a look at vectors. There’s an entire unharvested alternative in that.

Phillips: There’s worth in connecting these two. You speak about a vector we’d like for the design crew and a vector we’d like for the take a look at crew. The extra we are able to convey these two collectively and have an iterative, or collaborative, aligned knowledge set, the higher the understanding of what inputs and outputs have to occur on the chip. That’s the one of many keys to making an attempt to speed up by means of this course of. We have to bridge design to check and remove the proverbial wall that exists in product growth lifecycles.

SE: So principally the suggestions loop has to go a lot additional left and far additional proper?

Carmel: It must go a lot additional proper earlier than it may well go additional left. We have to undergo the device chain and use that knowledge to circle again and enhance the chips.

SE: One of many different challenges that we now have wanting ahead, along with security and design, is safety. That may have an effect on security and the worth of this complete system. How will we construct safety into these techniques?

Ciplickas: There may be positively a linkage between reliability and security and safety. There are quite a lot of angles to safety, however one factor I’m discovering is that a few of the strategies and measurements that you’d use to optimize reliability, can provide you instruments to extend safety. Debug displays or drift and shift displays, for instance, might detect sure sorts of assaults, whether or not it’s detected at t = 0, or whether or not it’s detected as irregular habits or drift within the subject. However those self same displays are already getting used for system operation and optimization. There’s correlated infrastructure between the 2, though they’re utilized in very alternative ways.

Carmel: We have to have a look at it as a chance for utilizing knowledge, because the extra worthwhile the information you produce, the higher the chip’s signature turns into. Ultimately, that knowledge lets you perceive if one thing is irregular. This may be much more urgent in shutdown-sensitive autos. Utilizing deep knowledge, you create 24/7 fleet visibility and establish issues as quickly as they happen.

SE: Given the quantity of knowledge that’s shifting by means of these techniques, are you truly going to have the ability to choose up a really slight anomaly, or is it simply going to be noise within the midst of all the opposite noise?

Carmel: What we offer is deep knowledge, based mostly on Common Chip Telemetry measurements. We’re delivering perception to the precise chip and system operation, efficiency, reliability margins and efficiency degradation. This real-world knowledge doesn’t depend on shifting contact factors, however on in-field operational outputs.

Ciplickas: To your level in regards to the sign and the noise, I’m optimistic the trade will have the ability to develop strategies to search out that sign. If you happen to have a look at the sensor knowledge that comes off a device whereas it’s processing a wafer or wire bond, the number of good alerts you may get is large. And the anomalies you discover in these alerts are generally tiny little blips. We’ve developed machine studying strategies to search out these tiny little blips within the sea of in any other case ‘good’ noise. As an alternative of occupied with it as a device making a wafer, if you concentrate on it as a system working within the subject, understanding these tiny blips is throughout the realm of chance. But it surely’s going to take quite a lot of work.

SE: Going again by means of the manufacturing cycle, are you discovering any glitches in your knowledge the place you’re going to say, ‘Okay, it is a potential safety threat that we didn’t perceive earlier than?’

Ciplickas: Understanding downstream alerts utilizing the upstream knowledge is a really highly effective method.

Baruch: Individuals typically have a tendency to take a look at predictive fashions as if they really are predictive. However they miss the truth that the characteristic set — the factor that’s truly contributing to your means to foretell one thing — is a very powerful half to filter noise and see what’s necessary and what’s not, and what’s the root reason for any points. We frequently use a shift-left mannequin, however that needs to be executed in an informed means. You don’t need to return to search for a needle in a haystack. Good fashions may help you discover what’s necessary and what’s not, so long as you resolve at which angle to take a look at them. Whenever you construct these fashions, you need to predict one thing. However you additionally want individuals who can return and repair the attributes in these fashions after they’re improper.

Ciplickas: Glorious level.

SE: An enormous hole appears to exist between ADAS versus autonomous autos. Transferring into full autonomy, it’s a must to begin considering of techniques of techniques working collectively. What occurs when you will have automobiles and gadgets on the street that use completely different generations of chips and completely different generations of software program as a result of they had been produced 10 years earlier?

Carmel: The basics of shifting from ADAS to AV is to know what sort of failures are skilled within the subject. Ultimately, it’s a matter of defining the efficiency envelope. Each automotive has its personal efficiency envelope, as a result of it has completely different {hardware}, completely different software program, completely different layers. When you already know precisely the best way to outline this efficiency envelope and create the stability between security, reliability and safety, then you will have management over the fleet. Utilizing deep knowledge, we are able to outline every mannequin and every unit’s standalone capabilities and define an autonomy hierarchy.

SE: Will we begin seeing AVs on the street in something apart from in geofenced areas, reminiscent of a single lane on a freeway arrange for autonomous autos, the place it’s a must to take over once you get off the freeway?

Carmel: The important thing to permitting autos to incrementally exit of a geofenced space is protection and scalability. When working exterior of a geofenced space, reliability and predictability will make sure that fail protected protocols might be adopted, and that requires absolute certainty in regards to the ECU’s operational capabilities and security profiles. This can solely be achieved with steady monitoring and non-intrusive in-field system integrity verification.

Ciplickas: It seems like a really pure evolution. You begin with an space in which you’ll be able to carry out properly, based mostly on that studying. I like that you just stated the geofenced space will develop. These would give us a ton of studying, which might then allow the subsequent ranges of autonomy.

van de Geijn: It’s not solely the price. It takes time to enhance the merchandise and parts and to study from them. Autonomous driving is just not one thing you simply swap on at some point and it exists. It’s going to enhance over the subsequent 10 years till you actually have one thing you are feeling comfy with, and which might do 80% or 90% of the issues a human can do.

SE: We appear to be a great distance from eradicating the steering wheels in automobiles.

Baruch: If you happen to have a look at the laws related to that, on one facet you will have China, which is sort of free on what they will do and what they management from a regulation standpoint. On the opposite facet, European international locations are fairly far-off from approving it. However this additionally overlaps with a second pattern, which is electrification for emissions management, and there may be a lot they will do in parallel, when introducing a brand new automobile to the market, that must be each absolutely autonomous and absolutely electrified. Given all of the fines and laws driving electrification, we’re seeing a a lot greater motion in that path versus the necessity to make absolutely autonomous automobiles shortly.

SE: Superior packaging in automobiles is new, as properly. We’ve had multi-chip modules for many years, however not just like the sorts of packages we’re seeing with sensor fusion or some 7/5nm chips. What affect does which have on reliability? Is it simply one other layer of complexity and knowledge that we now have to cope with? And do we now have to guarantee that all of the chips aren’t simply throughout the margin of acceptability by way of identified good die?

van de Geijn: It depends upon which a part of the automotive they’re going for use in. If they’re for the leisure system and people sort of issues, and you should use the identical parts that go into thousands and thousands of cellphones, you possibly can belief these components. When you have excessive failures within the cellphones, you’ll not use them anymore. Many corporations say, ‘That goes into leisure system and it’s a element that I can change by taking out the module and placing in a brand new module.’ That’s fully completely different from if these packages had been used, for instance, on your motor administration system. Corporations that make the buttons to maneuver a seat backwards and forwards might develop fully new applied sciences to switch these buttons after they don’t work anymore. But when it’s a motor administration unit, that’s a totally completely different story. It’s additionally the place you set them and the way you employ these components.

Carmel: Superior packaging provides one other layer of complexity as a result of it lacks visibility and depends on a high-density structure which limits redundancy fallbacks. As well as, the AI portion of the chips is rising. It’s not solely about packaging and superior nodes, however the truth that the chip structure is AI pushed and makes use of in-field inferencing and coaching to repeatedly enhance the {hardware} structure. Utilizing that suggestions loop, you possibly can scale back {hardware} redundancies and optimize complexities.

Baruch: Along with that, the packaging does add complexity to the notion of hierarchy and assembling parts. When you have one on high of the opposite, you’ll want to cross-correlate in three dimensions. That by itself introduces the semantic notion of the information. It has a number of vectors, and certainly one of them can be a hierarchy aspect. It does add complexity, as a result of once you have a look at a element you’re not seeing it as a single unit by itself. You’re additionally wanting on the hierarchy of the parts which might be a part of it. If you happen to don’t try this, you’re very a lot restricted in what you get out of that evaluation. Nonetheless, should you do that proper, it may be tremendous worthwhile for pinpointing the place an issue is.

Ciplickas: That comes again to the E142 spec, representing that hierarchy and understanding all of the relationships of all of the components which were positioned into this three-dimensional stacked bundle. The system-in-package, or 3D integration, goes to convey new fail modes because of the interplay between the parts. The chip-to-chip communication is completely different than chip-to-board communication, and the electrothermal/mechanical interactions are completely different. One carmaker confirmed that below stress, the SRAM failed in ways in which had been very predictable. They really measured these on a bench. That led to design guidelines within the PCB itself for the best way to construct mount factors in an ECU enclosure. That’s a macro model of the challenges which might be going to occur within the 3D or 2.5D integration in these packages, which will probably be put into harsh environments. So it’s not simply the chip-to-chip communication. Now, think about these items have completely different thermal profiles than you anticipate. That’s going to vary the growth and the stress on these items, which is then going to vary the efficiency, as a result of we all know the stress modifications the machine habits. Understanding the habits of the person chips at wafer kind take a look at, after which understanding what was put collectively in a bundle — and having the package-level analysis, and placing all that collectively — is a large problem. It’s an entire new frontier to make use of superior 2.5D integration in a automotive, and particularly in a safety-critical system.

[Uzi Baruch has since left Optimal Plus and joined proteanTecs as chief strategy officer.]


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