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Albemarle opens a Battery Materials Innovation Center in North Carolina

Albemarle certainly isn’t a household name, but it’s a major US-based producer of chemicals, particularly those used in the production of lithium batteries. Lithium batteries are key to all manner of electronic devices and are particularly critical for electric vehicles. The company has announced that it opened a Battery Materials Innovation Center (BMIC) and its Kings Mountain, North Carolina site.

The BMIC will be fully operational in July 2021 and will support the company’s lithium hydroxide, lithium carbonate, and advanced energy storage materials platforms. The facility is designed to enable the synthesis of new materials, material property characterization, and analysis. It also supports material scale-up capabilities and material integration into battery cells for performance testing.

The facility has a dry room with a multi-layer pouch cell line that can create cell phone-sized batteries to demonstrate critical aspects of performance and accelerate the transition of new products to customers. BMIC will also develop lithium metal anode technology to increase battery energy density using advanced lithium metal ruling to achieve lithium foils 20 microns thick. Twenty microns is about one-fifth the average thickness of a human hair.

The facility will demonstrate lithium foils even thinner with a thickness of 3 to 5 microns using new technologies currently under development. Albemarle says that its BMIC provides realistic and relevant cell building capability to generate data for next-generation battery material design. The company will leverage the resources to optimize the materials for creating a drop-in solution for customers to help deliver high-performance and cost-effective batteries to the electric vehicle market.

Albemarle is the only US-based producer of lithium metal anodes. The company says novel materials developed in its labs will enable the next frontier of lithium-ion battery performance. Moving from conventional graphite battery anodes to lithium metal offers the potential to double energy density and reduce cost by as much as 50 percent.

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AI

Applied Materials brings AI and big data into semiconductor inspection machines

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Applied Materials has launched a new generation of optical semiconductor wafer inspection machines that incorporate big data and AI techniques.

These multimillion-dollar machines are used in chip factories that can cost $22 billion to build and generate even more revenue than that. Such factories send wafers through hundreds of manufacturing steps before they’re finished and sliced into individual chips that are used in everything electronic.

With a severe shortage of such chips during the pandemic, Applied Materials’ latest improvements to the machines are timely, as the AI techniques enable the new Enlight optical wafer inspection systems to automatically inspect more chips and detect more killer defects that can ruin chips. These kinds of inspection machines alone add up to a $2 billion market worldwide.

Applied Materials executives like CEO Gary Dickerson have been predicting for years that the recent advances in AI would prove transformative in semiconductor manufacturing, and that’s what’s playing out now, Keith Wells, group vice president at Applied Materials, told VentureBeat in an interview.

“We all know that AI and big data have the potential to transform every area of the economy,” Wells said. “Today, that’s now reality. We’re bringing AI and big data into the semiconductor manufacturing.”

The new inspection systems are the fastest-ramping tools in the history of Santa Clara, California-based Applied Materials, which is the largest maker of equipment used in semiconductor factories. The machines speed time to revenue and help a chipmaker earn more profits over the life of a manufacturing process.

“We believe this is the industry’s fastest high-end optical inspector that is 3 times faster, and it has the sensitivity to find these yield-critical defects,” Wells said. “We believe it has the ability to impact the economics.”

The challenges

Chip manufacturing is getting more expensive and complex.

Above: Chip manufacturing is getting more expensive and complex.

Image Credit: Applied Materials

The challenge is that the costs of inspecting increasingly miniaturized patterns on wafers are rising, and the inspections are also becoming more complex. A decade ago, chip factory costs were about $9 billion. Now they’ve doubled. Over the life of the factory, the chipmaker can depreciate the cost of the chipmaking equipment in the factories. But manufacturing delays and inspection failures can cause factories to go idle (and lose a ton of money) as engineers try to decipher the cause of failures.

When it comes to memory chips, a week’s downtime can knock down annual output by 2%. On top of that, the price of the chips drops rapidly over time, and so falling behind schedule can severely damage revenue, Wells said. Add to this the notion that the inspection machines are getting more complicated and more expensive to produce.

“You don’t make money until you start ramping in volume in the millions of chips,” Wells said.

Dan Hutcheson, CEO of market analyst firm VLSI Research, said in a statement that being able to quickly and accurately distinguish killer defects is something chip engineers have struggled with for more than three decades. He said Applied Materials’ Enlight system with ExtractAI technology is a breakthrough approach that solves this challenge and added that because the AI gets smarter the more the system is used, it helps chipmakers increase their revenue per wafer over time.

In an email, Hutcheson said that Enlight can cut yield loss (the percentage of a wafer lost to defective chips) by $2.6 million for every hour it trims off the time to respond to a deviation from normal yields. He said inspection accounts for about 10% of the cost in an advanced wafer fab, and the current cost of such fabs is about $22 billion. That’s almost as much as two aircraft carriers and 65 F22 Raptor jets.

Semiconductor technology is becoming increasingly complex and expensive. So reducing the time needed to develop and ramp advanced manufacturing process nodes can be worth billions of dollars to chipmakers around the world.

But not being able to inspect chips fast enough is a barrier to speed. That’s a problem because it is increasingly hard to focus lenses so that you can see the surface of a chip, where the circuits are as little as five nanometers — or five billionths of a meter — apart. The tiniest specs of dust can be like boulders on the surface of a wafer.

That’s where the inspection machines come in. They can use AI to detect anomalies on the surface of a chip and then automatically fix the errors, if possible, so that the nuisance particles don’t ruin the circuitry.

“We’re looking for the defects that are effectively going to kill the device,” Wells said.

Above: Chip complexity is making inspection harder.

Image Credit: Applied Materials

For instance, if two circuit lines get crossed, that will divert electrical signals and possibly short circuit an entire chip. The inspection system uses a state-of-the-art scanning electron microscope, which helps identify the signals coming off the optical inspector to do classification of the flaws, Wells said.

“We’re going to take that classified data, and we’re going to feed it into an AI algorithm, which we call ExtractAI,” Wells said.

The result is creating actionable data for customers that lets them solve problems faster than ever. In the past, chipmakers have deployed more primitive AI, where the classification engine is static. It doesn’t have the ability to learn and adapt automatically. But chipmaking processes, or recipes for building chips, change frequently.

“The next necessary step is to allow the AI to learn and adapt,” Wells said. “As the process changes, they can adapt.”

Applied Materials said that 3D transistor formation and multiprocessing techniques introduce subtle variations that can multiply to create yield-killing defects that range from vexing and time-consuming to root-cause.

The company is solving these challenges with a new playbook for process control designed to bring the benefits of big data and AI technology to the core of chipmaking technology. Applied Materials’ solution consists of three elements it claims work together in real time to find and classify defects faster, better, and more cost effectively than legacy approaches.

The Enlight Optical Wafer Inspection System

Above: Enlight, ExtractAI, and SemVision are part of Applied Materials’ new inspection process.

Image Credit: Applied Materials

The AI comes in to make a decision about whether to slow the production speed down and alert a human about a problem in a wafer that carries a varying degree of risk.

In development for five years, the Enlight system combines industry-leading speed with high resolution and advanced optics to collect more yield-critical data per scan. The Enlight system architecture improves the economics of optical inspection, resulting in a 3 times reduction in the cost of capturing critical defects compared to competing approaches. The system has a more robust optical system — including features that put the equivalent of sunglasses on the optical lenses — to focus quickly on the problem parts of a wafer surface.

By dramatically improving cost, the Enlight system allows chipmakers to insert many more inspection points in the process flow. The resulting availability of big data enhances “line monitoring,” statistical process control methods that can predict yield excursions before they occur, immediately detect excursions so that wafer processing can be halted to protect yields, and enable root-cause traceback to accelerate corrective actions and the return to high-volume manufacturing.

“There are a lot of imperfections that engineers might not care about that your optical inspector will find, but it may not be a killer defect,” Wells said. “So the challenge is to give the customer actionable data.”

ExtractAI technology

Developed by Applied Materials’ data scientists, ExtractAI technology solves the most difficult problem of wafer inspection: the ability to quickly and accurately distinguish yield-killing defects from the millions of nuisance signals or “noise” generated by high-end optical scanners. It has to take a million possible problems and reduce them to 1,000 that can be inspected more closely.

ExtractAI creates a real-time connection between the big data generated by the customer’s optical inspection system and the eBeam review system that classifies specific yield signals so that by inference, the Enlight system resolves all of the signals on the wafer map, differentiating yield killers from noise.

ExtractAI technology is incredibly efficient; it characterizes all of the potential defects on the wafer map after reviewing only 0.001% of the samples. The result is an actionable map of classified defects that accelerates semiconductor node development, ramp, and yield. The AI technology is adaptive and quickly identifies new defects during high-volume production while progressively improving its performance and effectiveness as more wafers are scanned.

The ExtractAI tech uses high-resolution scans to detect the worst problems. Once the actual defects are removed, the system learns to adapt to better detection techniques the next time around. ExtractAI can reduce the number of problem areas from about a million to just about 1,000 that will need a closer look or some action.

“We interrogate the data, and we’re actually learning and adapting our classifying defects in real time,” Wells said. “This is different from other approaches where the classifiers are static.”

SemVision eBeam Review System

Above: Enlight has a complex optical system.

Image Credit: Applied Materials

The SemVision system is the most advanced and widely used eBeam review technology in the world, as 1,500 systems are in place at chip factories throughout the world. The SemVision system trains the Enlight system with ExtractAI technology to classify yield-killing defects and distinguish defects from noise.

By working together in real time, the Enlight system, ExtractAI technology, and SemVision system help customers identify new defects as they are introduced into the manufacturing flow, enabling higher yields and profitability. The large installed base of SemVision G7 systems is already compatible with the new Enlight system and ExtractAI technology.

“We’ve seen over the last five years the rise in capital costs of these inspectors, making the economics difficult,” Wells said. “Customers have been reducing inspections in order to compensate for the increase in the cost of these tools. But unfortunately, when you reduce inspection points, you get yield problems. The industry wants a better economic value message around doing more inspection. And we’re trying to provide that.”

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Categories
AI

IBM’s AI may lead to new antimicrobials, drugs, and materials

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In a new study published in the journal Nature Biomedical Engineering, researchers at IBM say they’ve developed an AI model that can assist in the rapid design of antimicrobial peptides — the building blocks of proteins. The researchers say that the model outperforms other AI methods at designing such peptides and increases the success rate of identifying a viable candidate by 10%.

Antibiotics have transformed the world of medicine over the past century or so, but they’ve also been overused, leading to the emergence of bacteria with powerful resistance. According to the Centers for Disease Control and Prevention (CDC), antibiotic resistance is one of the biggest public health challenges of our time. In fact, in the U.S. alone, nearly 3 million people die annually as a result of antibiotic-resistant infections.

Unfortunately, few new antibiotics are being developed to replace those that no longer work, in part because drug design is an extremely difficult, lengthy, and capital-intensive process. IBM’s proposed solution is generative modeling, a subfield of AI that allows researchers to decide upfront what characteristics they want peptides to have versus guessing combinations.

Historically, material design of molecules, proteins, and altogether new peptides has been a complex simulation problem. Even small molecules made of only a few atoms have hundreds of possible combinations. To combat this, IBM’s AI model pulls from a large dataset to reverse-engineer a peptide’s design and produce the desired peptide framework. Effectively, it shortens the time needed to create high-quality peptide candidates from years to potentially days while increasing the likelihood of identifying successful candidates to fight antibiotic drug resistance.

Within 48 days, IBM says its AI-boosted molecular design approach enabled it to identify, synthesize, and experimentally test 20 AI-generated novel candidate antimicrobial peptides. Two of them turned out to be potent against pathogens, very unlikely to trigger drug resistance in E. coli, and had low toxicity when tested both in vitro and in mice.

Beyond antibiotics, IBM says the generative AI system could potentially accelerate the design process of molecules for new drugs and materials. “Our proposed approach could potentially lead to faster and more efficient discovery of potent and selective broad-spectrum antimicrobials to keep antibiotic-resistant bacteria at bay — for good,” IBM’s Saska Mojsilovic and Payel Das wrote in a blog post. “And we hope that our AI could also be used to help address the world’s other most difficult discovery challenges, such as designing new therapeutics, environmentally friendly and sustainable photoresists, new catalysts for more efficient carbon capture, and so much more.”

IBM’s latest work builds on an earlier study published in the journal Advanced Science by the company’s researchers. It demonstrated a technique that enabled the coauthors to create up to 100 bacteria-fighting polymers in nine minutes, using AI and machine learning.

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Categories
AI

IBM launches AI platform to discover new materials

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IBM today announced the launch of the Molecule Generation Experience (MolGX), a cloud-based, AI-driven molecular design platform that automatically invents new molecular structures. MoIGX, a part of IBM’s overarching strategy that aims to accelerate the discovery of new materials by 10 to 100 times, uncovers materials from the property targets of a given product.

The chemical sciences have made strides in the discovery of novel and useful materials over the past decades. For example, in the area of polymers, the recent development of thermoplastics has had an influence on applications ranging from new paints to clothing fibers. But while the discovery of new materials is the driving force in the expansion and improvement of industrial products, the vastness of chemical space likely exceeds the ability of human experts to explore even a fraction of it.

By observing and selecting a dataset, MolGX leverages generative models to produce molecules from chemical properties like “solubility in water” and “heatability.” The platform trains an AI model to predict chemical characteristics within given parameters and synthesizes molecular structures based on the model built.

“The development of new materials follows a number of different pathways, depending on both the nature of the problem being pursued and the means of investigation. Breakthroughs in the discovery of new materials span from pure chance, to trial-and-error approaches, to design by analogy to existing systems,” Seiji Takeda, technical lead of material discovery at IBM, wrote in a blog post. “While these methodologies have taken us far, the challenges and requirements for new materials are more complex — so too are the demands and issues for which new materials are needed. As we face global problems such as pandemics and climate change, the necessity and urgency to design and develop new medicines and materials at a faster pace and on a molecular scale through to the macroscopic level of a final product is becoming increasingly important.”

IBM has released a free trial version of MolGX trained using a built-in dataset, which the company applied internally to the development of a new photoacid generator — a key material in electronics manufacturing. A professional version of MoIGX with additional functionality including data upload, results exportation, customized modeling, and more is available with a license. According to IBM, this paid release carried out the inverse design of sugar and dye molecules over 10 times faster than human chemists at Nagase & Co Ltd, a chemical manufacturing company.

Beyond IBM, startups like Kebotix are developing AI tools that automate lab experiments to uncover materials faster than with manual techniques. Meanwhile, Facebook and Carnegie Mellon have partnered on a project to discover better ways to store renewable energy, in part by tapping AI to accelerate the search for electrocatalysts, or catalysts that participate in electrochemical reactions.

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