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Legacy Process Nodes are Critical to Many Industries

Stephanie Cochran — Thu, 05 Sep 2024 15:28:15 +0000

PHYSICS, MACHINES AND DATA

Legacy Process Nodes are Critical to Many Industries

5. September 2024/inTignis News

As the semiconductor industry continues to push the boundaries of innovation with advanced nodes, it is easy to overlook the critical role that ICs manufactured at legacy process nodes play in our everyday lives.

While the spotlight often shines on the leading-edge advancements of 5nm technology and below, it’s the mature nodes, those above 28nm and even above 130nm, that are the unsung chips that underpin a vast array of industries and applications. These mature node devices are indispensable, providing the backbone for electronic systems from automotive electronics to consumer products and industrial equipment.

Looking into the challenges and opportunities facing legacy semiconductor manufacturers, you will learn why maintaining robust production capabilities for these nodes is more important than ever.

The Automotive Industry: A Case Study in Dependence on Legacy Nodes

The automotive industry offers a stark reminder of the importance of legacy nodes. During the COVID-19 pandemic, the world witnessed a dramatic slowdown in vehicle production due to a shortage of semiconductors. Notably, it wasn’t the most advanced chips that were the primary issue, but the mature node devices that were essential for basic automotive functions such as steering, braking and engine control.

The situation became so dire that some automakers were forced to halt production lines resulting in more than 600,000 fewer vehicles produced in Q1 2021 according to HIS Markit. This crisis highlighted the fragility of today’s global supply chains and underscored the critical need for reliable access to legacy semiconductors. As vehicles become increasingly electrified, the demand for legacy chips (in addition to advanced ICs) will only grow, cementing their importance even more.

Beyond Autos: Everyday Essentials Powered by Legacy Nodes

While the automotive sector is a very high-profile example, semiconductors manufactured at mature process nodes are essential across many other industries as well. Consider the everyday items that we often take for granted: televisions, refrigerators, coffee machines, and Wi-Fi routers. These products all rely on mature semiconductor process technology. Unlike their advanced counterparts, which are typically focused on high-performance computing and specialized applications, legacy nodes offer the perfect balance of performance, cost, and reliability for mass-market high-volume products.

Their well-established manufacturing processes result in higher yields and lower defect rates, translating into cost-effective products for consumers, and predictable profitability for manufacturers. For these reasons, the demand for legacy nodes remains strong.

Industrial and Military Applications

Legacy nodes are also critical in industrial and military applications where reliability and durability are paramount. Industrial control systems, used in manufacturing equipment and process automation, rely on mature node ICs to ensure stable and uninterrupted operations. The ability to perform consistently in harsh environments makes these chips ideal for such applications.

In the military sector, legacy semiconductors are used in avionics, communication systems, radar, and missile guidance systems. These applications require chips that can withstand extreme conditions and provide long-term reliability, traits that are hallmarks of mature node process technology.

The Pandemic’s Legacy Chip Shortage: A Wake-Up Call

The COVID-19 pandemic served as a stark reminder of American (and global) dependency on legacy semiconductors. According to the U.S. Department of Commerce, firms faced their most acute shortages in chips at the 40nm node or larger. This shortage was a result of disrupted supply chains and a surging demand for consumer electronics and work-from-home devices, which further stressed production capacity for legacy chips. As consumers found out, the semiconductor industry cannot simply add more production capacity, it takes years to bring additional manufacturing capacity online.

This situation highlighted the critical need to enhance domestic manufacturing capabilities for both legacy and advanced semiconductors. The U.S. automotive and electronics industry’s reliance on these chips underscores the economic and strategic importance of maintaining robust legacy (and advanced) manufacturing capabilities.

The Path Forward for Legacy Manufacturers

For legacy semiconductor manufacturers, the path forward requires a delicate balance act between maintaining and upgrading existing fabs and investing in new production capabilities. Many legacy fabs are now over two decades old with equally aged manufacturing equipment and most likely with a significant percentage of its workforce nearing retirement. AI/ML can provide legacy fab managers with a two-pronged approach to these challenges: Improving fab profitability through higher OEE by leveraging all the data generated and collected across the fab to identify critical process and maintenance issues before they negatively impact manufacturing. The second benefit of AI/ML is to preserve the institutional and tribal process knowledge in your subject matter experts and democratize it across your entire manufacturing workforce.

For semiconductor companies building new fabs to increase capacity, the capital outlay is enormous, and it is vital to have those fabs reach high-volume production as soon as possible. AI/ML can help here as well, by leveraging the optimized process and equipment maintenance knowledge that was captured by the machine learning models from your existing fabs and using that knowledge to accelerate the yield ramp in your new fab.

Legacy process nodes are the unsung heroes of the semiconductor industry. Their critical role in powering a wide range of applications, from automotive and consumer electronics to industrial and military systems, cannot be overstated. As we continue to navigate the post-pandemic world, the importance of maintaining strong production capabilities for these mature nodes is more evident than ever.

For a deeper dive into the significance of legacy nodes and their role in the semiconductor industry, I encourage you to read the comprehensive SemiEngineering article that inspired this blog. Understanding the nuances and complexities of legacy semiconductor manufacturing will help us appreciate the vital role these technologies play in our modern world.

Read the full SemiEngineering article https://lnkd.in/e7vsUT4G. Stay informed and stay ahead in the ever-evolving world of semiconductors.

#Semiconductor #AI #ML #Innovation #Tignis #LegacyNodes #AutomotiveIndustry #TechInnovation #ConsumerElectronics

References:

“Legacy Process Nodes Going Strong,” SemiEngineering: https://semiengineering.com/legacy-process-nodes-going-strong
Marco Mezger LinkedIn post: https://www.linkedin.com/posts/marcomezger_chips-manufacturing-technology-activity-7221746622454525953-P3FI?utm_source=share&utm_medium=member_desktop
https://time.com/6075425/semiconductor-chip-shortage/, https://www.appliedenergysystems.com/semiconductor-shortages-effect-on-auto-industry/
https://polarsemi.com/blog/blog-semiconductor-chips-in-a-car/#:~:text=On%20average%2C%20you’ll%20find,That’s%20a%20lot

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Why Every Fab Should Be Using AI

Stephanie Cochran — Wed, 05 Jun 2024 18:40:12 +0000

PHYSICS, MACHINES AND DATA

Why Every Fab Should Be Using AI

5. June 2024|inTignis News

At CS Mantech 2024, Charlie Parker, Sr. Machine Learning Engineer at Tignis presented on “Why Every Fab Should Be Using AI”.

If you missed his presentation, you can watch it below.

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Navigating the Talent Crunch

Stephanie Cochran — Mon, 06 May 2024 19:40:24 +0000

PHYSICS, MACHINES AND DATA

Navigating the Talent Crunch: AI Solutions for a Thriving Semiconductor Manufacturing Sector

6. May 2024|inTignis News

The CHIPS and Science Act is a historic piece of legislation passed by the US government in 2022 aimed at regaining American leadership in semiconductor manufacturing. Supported by an unprecedented $52 billion in federal funding, this investment will also address supply chain vulnerabilities and national security concerns that were made glaringly public by the COVID epidemic.

In addition to revitalizing our chip manufacturing prowess and capacity, the CHIPS Act will also drive us to address another vital necessity —the need for a strong talent strategy. Beyond the funding of many new fabs, the Act will create demand for thousands of technical jobs needed to operate these fabs, talent that is in very short supply based on the projected growth rate of the semiconductor industry. America and the semiconductor industry will need to focus on establishing a much larger pool of trained workers to staff all the new fabs that will come online through the end of the decade.

The New Reality: A Talent Strategy for Tomorrow

In today’s high-tech world, creating jobs alone is no longer adequate. For the industry to thrive, a methodical approach to developing and retaining a skilled workforce capable of managing the complexities of modern semiconductor manufacturing is needed.

In the past, the assumption was that there would be an adequate supply of individuals possessing the required abilities to fill open positions. However, the semiconductor industry today is not a market segment that is actively sought after by high school or even college graduates. Combined with an aging workforce where significant numbers of workers will be retiring in a few years, it makes the need for trained technicians even more pressing for semiconductor manufacturers.

We need strategies that not only focus on training new workers but also on continuously enhancing the skills of existing employees to keep pace with the experienced staff expected to leave the workforce through the end of the decade. We need to look beyond just training new employees but also enabling existing technical staff to operate more efficiently and retain institutional and tribal knowledge before it disappears.

Building More Than Just Semiconductors: Building AI Assets That Increase in Value

In the face of this growing need for skilled labor in the semiconductor industry, technological innovations such as AI and machine learning are stepping in to bridge this gap. Companies like Tignis are at the forefront of this transformation, developing AI-powered process control solutions to significantly enhance the efficiency and precision of semiconductor manufacturing.

Tignis leverages advanced AI algorithms to monitor and optimize the complex processes involved in semiconductor manufacturing. These AI systems can predict and prevent potential issues before they occur, reduce waste, and ensure that tool availability is maximized. By automating and optimizing these critical goals, Tignis not only increases the yield of semiconductor production but also frees up human workers to be reallocated on more strategic, creative tasks that require human insight.

In today’s paradigm, manufacturing knowledge is captured, learned, and retained by expert individuals. Typically, semiconductor process engineers are taught the theoretical levers and environmental factors that affect their process, but at the same time they also need to learn the company-specific subtleties of their fab, tools, and products that make their process recipes efficient and dependable. But as paradigms shift and experts retire, critical institutional knowledge can be difficult to retain. AI is a highly valuable mechanism that does more than just learn the theoretical physics of a process, it also captures the nuances of that process over time in human-readable code. This AI code then becomes a persistent knowledge database for future engineers, democratizing the tribal knowledge that was once siloed in specific individuals.

Expert Insight

The integration of AI and automation into the semiconductor industry is often met with apprehension, with concerns about job displacement prevalent among the workforce.

David Park, VP of Marketing at Tignis has a more optimistic viewpoint in these areas saying, “AI and automation are not just about making processes faster or more cost-effective; they are about fundamentally transforming what a job entails. They enable us to rethink how and where we deploy human creativity and skills.”

This insight is crucial in shifting the narrative around AI from one of threat to one of opportunity. Park’s perspective highlights that AI and automation do not merely replace human labor but rather redefine it. They free human workers from mundane and repetitive tasks, allowing them to focus on more complex, strategic activities that machines are not equipped to manage.

In the context of ongoing labor shortages in the tech industry, AI and automation present a viable solution. They compensate for the lack of available human workers and help ensure that production does not stall due to workforce constraints. More importantly, they create an environment where all available human talent is used more effectively, maximizing the output and innovation potential of each employee. In this light, AI and automation are essential tools in the evolution of the workforce. They are not just adapting the industry to modern challenges but are also setting the foundation for future growth and development.

Turning the Tide with AI and Automation

The U.S. semiconductor manufacturing industry is currently facing a significant talent crunch, a challenge that is being addressed through the strategic integration of AI and machine-learning. These technologies are fundamentally revolutionizing the landscape of manufacturing, learning, and future growth.

But time is of the essence. AI can only learn from what it has seen in the past. Companies that wait to implement an AI strategy are losing valuable learning cycles and will be left behind by those that have already put AI strategies into place.

Tignis is at the forefront of this transformative journey. With a laser-focused commitment to the semiconductor industry, Tignis is not just responding to industry trends but actively shaping the future of semiconductor manufacturing. By prioritizing the development and implementation of AI and machine-learning, Tignis is envisioning a workforce that is quick, responsive, and leverages the knowledge that has been aggregated over decades of successful high-volume manufacturing.

References:

Howard, K. (2024, March 25). Chip market in the US: Influence of the CHIPS Act in GovCon. GovCon Wire. https://www.govconwire.com/articles/chip-market-in-us-chips-act-govcon

Morra, J. (2023, August 9). U.S. semiconductor workforce shortage reaching critical stage. Electronic Design. https://www.electronicdesign.com/technologies/embedded/article/21270688/electronic-design-us-semiconductor-workforce-shortage-reaching-critical-stage

House, W. (2023, February 3). FACT SHEET: CHIPS and Science Act will lower costs, create jobs, strengthen supply chains, and counter China. The White House.

Collins, B. (2024, February 19). US considering more than $10 billion in subsidies for Intel as part of CHIPS act to secure domestic semiconductor… TechRadar. https://www.techradar.com/pro/us-considering-more-than-dollar10-billion-in-subsidies-for-intel-as-part-of-chips-act-to-secure-domestic-semiconductor-manufacturing

Semiconductors and Artificial Intelligence – IEEE IRDSTM. (n.d.). https://irds.ieee.org/topics/semiconductors-and-artificial-intelligence#:~:text=AI%20demands%20will%20have%20lasting%20impacts%20on%20semiconductor%20design%20and%20production

Allan, L. (2024, April 4). AI takes aim at chip industry workforce training. Semiconductor Engineering. https://semiengineering.com/ai-automation-to-help-train-workforce-preserve-legacy-knowledge-optimize-processes

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A Blueprint for Semiconductor Manufacturing Success with AI/ML Adoption

Stephanie Cochran — Mon, 22 Apr 2024 18:30:52 +0000

PHYSICS, MACHINES AND DATA

A Blueprint for Semiconductor Manufacturing Success with AI/ML Adoption

22. April 2024|inTignis News

In semiconductor manufacturing, the integration of AI and ML (machine learning) is not just a passing trend; it is a seminal shift that is already enhancing many facets of high-volume manufacturing (HVM). The integration of these technologies is driving enhanced operational efficiency, improved product quality, and reduced maintenance costs. Let’s explore the specific areas where these technologies are making an impact today.

Process Optimization: Machine learning models excel at automating and optimizing semiconductor manufacturing processes. By analyzing process and equipment data, ML algorithms can identify subtle and complex issues that traditional methods cannot recognize. The rapid troubleshooting and root-cause-analysis capabilities of AI/ML solutions enable process engineers to proactively address manufacturing issues, driving improved product throughput and lower cost of goods sold (COGS). This is especially beneficial in periods of high semiconductor demand when greater equipment availability has a direct and positive impact on product revenue.

Intelligent Maintenance: Equipment downtime is a necessary evil in semiconductor manufacturing. Tools need to be well-maintained in order for them to properly manufacture devices that inherently have tight manufacturing tolerances. Performing too much maintenance is expensive and reduces equipment availability. Insufficient maintenance (e.g. run to fail) can be equally expensive if it results in “long term down” situations in addition to potentially scrapping the wafers in process when a tool goes down. AI/ML delivers intelligent maintenance, identifying equipment failures before they occur, enabling equipment engineers to leverage scheduled downtime to include additional maintenance tasks that can eliminate unplanned and costly maintenance activities.

Equipment Inventory Management: Legacy semiconductor manufacturers have additional challenges because many of the tools used in their fabs can be decades old. Maintaining a proper supply of critical replacement parts is essential to maintain high-volume manufacturing, especially for parts with limited availability. Even for companies that choose “run to fail” manufacturing practices, AI/ML can provide clear visibility into the need for future replacement parts, enabling equipment support teams to be ready to address any equipment down situation with minimal impact on production throughput.

Quality Control: In any manufacturing environment, process drift has always been a challenge for process engineers. Even the best designed processes will experience drift over time resulting in process variability than can affect quality. AI/ML models are being used today to implement “run to run” process control that can actively manage process drift. Not only can process variation be reduced by 50% or more, chamber availability can also be increased by 1-3% providing additional capacity from the same tool fleet.

Cost Reduction: One of the most significant impacts of AI and ML in semiconductor manufacturing is overall cost reduction. Every one of the aforementioned areas has a direct impact on cost reduction: Engineering efficiency, smarter maintenance and inventory management, and improved product quality all contribute to lower COGS for semiconductor manufacturers.

Challenges and Future Directions: Despite the clear advantages, the integration of AI/ML into existing semiconductor manufacturing processes is not without challenges. High-quality data is the lifeblood of AI, and ensuring the timely availability of high-fidelity equipment and process data is essential to the success of any AI/ML implementation. Process engineering and equipment automation teams also need to be open to change, because AI/ML is much more than just “doing the same thing, but faster”. AI/ML is already changing the manufacturing paradigm that has existed for decades, and will continue to do so. Those that embrace change will be the winners in the next era of semiconductor manufacturing.

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The future of AI/ML is bright. As these technologies continue to advance, they will continue to unlock untapped potential within the semiconductor industry. In fact, other than fab personnel, AI/ML is the only appreciating asset in your fab. We can expect to see further improvements in efficiency, reductions in production costs, and the development of even more sophisticated chips primarily due to the benefits of AI/ML.

For Tignis, these advancements are particularly pertinent. Tignis is well-positioned to help semiconductor manufacturers and equipment OEMs to achieve new levels of automation and process control, ensuring that these companies remain at the leading edge of innovation.

AI/ML is not just supporting the semiconductor industry, it is reshaping it. As an industry, we are on the cusp of a new era, with AI/ML acting as the catalysts for innovation.

Sources:

McKinsey & Company. (2024, March 13). Insights on Artificial Intelligence. https://www.mckinsey.com/capabilities/quantumblack/our-insights

McKinsey & Company. (2021, April 2). Scaling AI in the sector that enables it: Lessons for semiconductor-device makers. https://www.mckinsey.com/industries/semiconductors/our-insights/scaling-ai-in-the-sector-that-enables-it-lessons-for-semiconductor-device-makers

Intel Newsroom. (2024, February 21). Intel Launches World’s First Systems Foundry Designed for the AI Era. https://newsroom.intel.com/intel-foundry/foundry-news-roadmaps-updates

Intel Newsroom. (2023, November 3). Intel Labs Introduces AI Diffusion Model, Generates 360-Degree Images. https://newsroom.intel.com/artificial-intelligence/intel-labs-introduces-ai-diffusion-model-generates-360-degree-images-from-text-prompts

TechOvedas. (2023, August 7). How AI is Revolutionizing the Semiconductor Industry. https://techovedas.com/how-ai-is-revolutionizing-the-semiconductor-industry/

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Transforming Semiconductor Manufacturing with AI and ML

Stephanie Cochran — Mon, 30 Oct 2023 15:49:13 +0000

PHYSICS, MACHINES AND DATA

Transforming Semiconductor Manufacturing: How AI and ML Boost Productivity and Beat the Skill Shortage

30. October 2023|inTignis News

In the fast-paced world of semiconductor manufacturing, where innovation and efficiency are essential, there is a serious challenge- the persistent skilled labor shortage. As evident by billboards looking for workers along major highways, this shortage is not just a concern but a pressing reality.

Semiconductor manufacturers in the United States face a multifaceted problem—tightened immigration policies limiting access to skilled and unskilled workers, and a lack of graduates in STEM fields, critical for the industry’s growth. In Oregon, where Analog Devices, Intel and Microchip have fabs, the semiconductor sector expects to add more than 6,000 jobs over the next few years as these manufacturers expand.(1)

The demand for semiconductor manufacturing expertise has never been greater, and this is where Artificial Intelligence (AI) and Machine Learning (ML) become very useful. Not by replacing people but by helping the teams you have become more efficient.

Bridging the Skills Gap

The talent shortfall within the semiconductor industry is a profound issue that affects both process engineers and management alike. AI and ML have emerged as powerful tools to address this predicament, and here’s how they do it:

Swift Onboarding: The semiconductor industry demands specialized knowledge and skills. AI and ML solutions can expedite the onboarding process for new employees. Interactive simulations, comprehensive training modules, and personalized learning experiences allow inexperienced workers to grasp complex manufacturing processes and equipment more efficiently. AI-driven training modules reduce the learning curve and ensure that newcomers become proficient swiftly.
Codifying Institutional Knowledge: Experienced engineers and managers play a pivotal role in the industry’s success. However, as many of them approach retirement or move to other companies, there’s a genuine concern that their institutional knowledge may be lost. AI can preserve this wisdom by codifying and storing it in digital repositories, making it accessible to all. New hires can benefit from the collective wisdom of their predecessors, ensuring a smoother transition and enhanced performance.
Predictive Maintenance and Troubleshooting: One of the industry’s primary challenges is the efficient maintenance of highly complex equipment. AI/ML-powered predictive maintenance systems can forecast equipment failures and suggest maintenance schedules. This not only reduces downtime but also enhances overall efficiency. Furthermore, these systems can swiftly identify and troubleshoot problems, allowing engineers to tackle issues with precision and agility.
Enhanced Productivity: AI/ML-powered automation takes care of routine and repetitive tasks, freeing up engineers and management to focus on more creative and complex challenges. As a result, employees can apply their expertise where it’s needed the most, driving productivity and innovation.
Problem-Solving at Speed: The semiconductor manufacturing process relies heavily on the functionality of complex machinery. AI and ML are adept at analyzing vast datasets and identifying intricate problems quickly. This accelerates the troubleshooting process and ensures that production processes continue without significant disruption.

AI and ML: A Competitive Edge

In the semiconductor industry, where competition is fierce, embracing AI and ML is not an option but a necessity. Leading companies have already recognized the potential of these technologies in driving efficiency and innovation, setting a precedent for the rest of the industry.

By leveraging AI and ML, semiconductor manufacturers in the United States can transform their operations, maximize their existing talent pool, and maintain a competitive edge in the global market. As the industry confronts the challenges of recruitment, retention, and skills development, these technologies offer a lifeline, ensuring that progress and productivity remain unwavering.

Embrace the Future

AI and ML are not just tools; they are our partners in progress and are the keys to addressing the skilled labor shortage. By integrating them into our operations, we do more than secure the future of our industry; we bolster our professional growth and elevate our collective capabilities. Industry leaders have set the precedent by adopting AI/ML, and we can all leverage the power of these transformative technologies.

In the evolving semiconductor landscape, the union of human expertise and AI intelligence is our pathway to sustained productivity and innovation. By embracing AI and ML, we secure our role as leaders in a dynamic, competitive, and fast-evolving industry. It is not merely an option; it is the means to maintain our status as a powerhouse of progress and ingenuity.

Resources

“Oregon seeks to jump start semiconductor workforce.” OregonLive. https://www.oregonlive.com/silicon-forest/2023/10/oregon-seeks-to-jump-start-semiconductor-workforce-with-intensive-two-week-program.html

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Optimum Energy and Tignis Enter Exclusivity Agreement

Stephanie Cochran — Thu, 06 Jul 2023 15:06:03 +0000

PHYSICS, MACHINES AND DATA

Optimum Energy and Tignis Enter into a Worldwide Exclusivity Agreement to Advance Efficiency for HVAC Solutions

6. July 2023|inTignis News

PAICe Monitor’s predictive analytics enhances Optimum’s ability to reduce energy costs and carbon emissions for its customers across semiconductor fabs, pharmaceutical manufacturing, hospitals, universities, and other smart buildings

SEATTLE, July 6, 2023 – Optimum Energy (“Optimum”), a leading supplier of energy optimization solutions, and Tignis, Inc. (“Tignis”), a pioneer in AI/ML solutions for advanced process control, today announced a new multi-year worldwide exclusive agreement to provide a closed loop HVAC solution for smart buildings worldwide, including for semiconductor fabs, pharmaceutical manufacturers, hospitals, and universities, among others. PAICe Monitor from Tignis will be embedded into Optimum’s control software solution to rapidly identify operational anomalies that negatively impact energy usage and carbon emissions in built environments worldwide. Optimum customers will benefit from additional operating cost savings and improved sustainability for their ongoing operations.

PAICe Monitor is a production-proven, AI-powered analytics solution that ingests existing operational data and learns the nuances of a customer’s process to automatically detect anomalies that can lead to costly operational variances in their built environment. In parallel, PAICe Monitor accelerates root cause analysis by identifying the most highly correlated interactions of system inputs and environmental variables, enabling support teams to proactively address issues that impact operational excellence.

Optimum’s state-of-the-art control software provides continuous system-level optimization of water-cooled chilled water plants, air handling units, boiler plants, among other energy system equipment, without requiring operator intervention. The software acts as a seamless intelligent supervisor for a customer’s HVAC building automation system. Regardless of building load or ambient conditions, Optimum’s technology automatically delivers the most efficient operation of an entire energy system resulting in increased equipment life, and energy and cost savings of up to 50%.

“Tignis is hyper-focused on leveraging AI to autonomously optimize process control and improve operational efficiency in advanced manufacturing environments,” said Jon Herlocker, CEO and Co-Founder of Tignis. “We are delighted to partner with Optimum to leverage our AI-powered process control solutions and we look forward to helping them continue to expand that capability to all of their global customers.”

“We are pleased to enter into this multi-year engagement with Tignis,” said Larry Stapleton, President of Optimum Energy. “Tignis is a leader in their field, and their technology is enhancing our ability to achieve double-digit improvement in energy savings and reliability for our customers. We are looking forward to expanding our use of Tignis technology across our portfolio of customers.”

About Optimum Energy

Since 2005, Optimum Energy’s patented software and engineering expertise has helped customers reduce energy use in heating and cooling systems, the largest consumer of energy in buildings, by up to 50%. Our solutions combine technologically advanced HVAC optimization software with powerful cloud-based data analytics and world-class engineering support. It’s a proven, measurable approach that verifiably reduces energy and water usage, while also resulting in significantly improved operations. From dramatic energy reductions to improved business continuity, from better asset management capabilities to powerful tools and engineering support that augment your existing facilities staff capabilities, Optimum Energy has the complete solution for maximizing your HVAC system’s operational efficiency.

About Tignis

Tignis is a leader in AI-powered process control solutions to enable next generation manufacturing processes, increase manufacturing output, and improve manufacturing sustainability. The company’s products democratize AI and machine-learning, enabling process and operations engineers to design, test and achieve extraordinary AI/ML results in manufacturing environments. Tignis is headquartered in Seattle, WA and the company’s technology is deployed in hundreds of facilities worldwide.

Contacts

Gaige Baisch
Marketing Manager, Optimum Energy
gaige.baisch@optimumenergyco.com
206-582-3192

David Park
VP of Marketing, Tignis
david.park@tignis.com
503-913-4793

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Tignis Joins MITRE Engenuity’s Semiconductor Alliance

Stephanie Cochran — Thu, 02 Feb 2023 15:28:09 +0000

PHYSICS, MACHINES AND DATA

Tignis Joins MITRE Engenuity’s Semiconductor Alliance to Accelerate Next-Generation Technological Advancements Across the U.S. Semiconductor Industry

2. February 2023|inTignis News

Tech Foundation MITRE Engenuity Convenes Experts, Organizations, and Investors to Address American Semiconductor Innovation, Driving Generational Impact for Public Good

SEATTLE, Wash., February 2, 2023 – Tignis, a technology innovator in AI process control for semiconductor manufacturing, today announced it has joined MITRE Engenuity’s Semiconductor Alliance. In alignment with the coalition’s focus on collaboratively defining breakthrough semiconductor industry advancements critical to a healthy economy and national security, Tignis brings advanced expertise in artificial intelligence, machine learning, physics and data science.

“We are proud to be part of MITRE Engenuity’s Semiconductor Alliance, working together to define and accelerate our nation’s position of leadership across the semiconductor industry,” said Jon Herlocker, president and CEO of Tignis. “MITRE Engenuity brings together a distinguished coalition of innovators throughout every aspect of the supply chain, collaboratively identifying the best pathways and investments for semiconductor technology innovations and manufacturing leadership. We will all benefit from the resulting shared vision and U.S. preeminence throughout this critical industry.”

“We’re thrilled to have Tignis join our collaboration of industry-leading companies from across the United States that collectively account for over 50% of the industry’s R&D share,” said Raj Jammy, chief technologist, MITRE Engenuity, and executive director of the Semiconductor Alliance. “We’re working together to grow the semiconductor industry on U.S. soil and regain domestic leadership in the industries of today and the future.”

In alignment with the esteemed MITRE Engenuity Semiconductor Alliance members, Tignis brings unique expertise for driving next-generation semiconductor manufacturing advancements. Through its PAICe product suite, Tignis provides the precise insights of physics with the most advanced AI and ML data science to give semiconductor equipment manufacturers, wafer fabs, and components and materials suppliers unprecedented automation and process control. The company’s unique physics-driven AI models enable engineers and data scientists to understand how their equipment will operate in modes never previously observed. Tignis delivers the ability to know what equipment will do, select the best states of operation, and continually optimize processes.

About MITRE Engenuity

MITRE Engenuity, a subsidiary of MITRE, is a tech foundation for public good. MITRE’s mission-driven teams are dedicated to solving problems for a safer world. Through the organization’s public-private partnerships and federally funded R&D centers, MITRE Engenuity works across government and in partnership with industry to tackle challenges to the safety, stability, and well-being of our nation. MITRE Engenuity brings MITRE’s deep technical know-how and systems thinking to the private sector to solve complex challenges that government alone cannot solve. MITRE Engenuity catalyzes the collective R&D strength of the broader U.S. federal government, academia, and private sector to tackle national and global challenges.

About Tignis

Tignis specializes in AI-powered process control with a physics and engineering foundation. Headquartered in Seattle, the company develops and sells innovative software solutions that use AI and machine learning to enable next-generation manufacturing processes. Tignis gives semiconductor equipment manufacturers, wafer fabs, and components and materials suppliers unprecedented automation and process control—increasing manufacturing yield, decreasing process downtime, and reducing costs. Working with the world’s top semiconductor equipment manufacturers and fabricators, Tignis also has a proven track record of empowering other large-scale mission-critical industries. Tignis solutions are deployed in hundreds of facilities worldwide.

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Tignis Announces Commercial Release of PAICe Product Suite

Antoinette McKinley — Wed, 25 Aug 2021 17:31:08 +0000

PHYSICS, MACHINES AND DATA

Tignis Announces Commercial Release of PAICe Product Suite

25. August 2021|inTignis News

Technology Innovator Tignis Launches New AI and Machine Learning Tool Suite for Manufacturing and Process Control that Puts the Power of Machine Learning in the Hands of Non-Data Scientists

Includes The Ability to Utilize Tignis-Created Surrogate Machine Learning Models That Are Up to One Million Times Faster than Today’s Physics-based Simulations

SEATTLE, Wash. – Tignis, a technology innovator in AI-Powered process control with a physics and engineering foundation, today announced the PAICe Product Suite, a new AI and Machine Learning analytics tool set for manufacturing and process control that puts the power of Machine Learning in the hands of non-data scientists. This next-generation technology helps manufacturers achieve process improvements not previously possible with advanced process control (APC), including the ability to utilize surrogate machine learning models created by Tignis that are more accurate and up to one million times faster than physics-based simulations, resulting in faster production, better quality control and faster time to market.

At the PAICe Product Suite’s core is a brand new, low-code programming language built by Tignis called DTQL (Digital Twin Query Language), the first language designed specifically to build machine analytics on digital twins. Through DTQL, the PAICe product suite significantly removes the obstacles that have prevented engineers from leveraging all the historical data they have collected into better decisions, and enables process and reliability engineers to convert their deep subject matter knowledge into hundreds of machine learning based predictive models that are easily managed across thousands of diverse physical assets – without having to become a data-scientist.

The PAICe product suite accelerates the ability to build, validate and deploy machine learning enabled solutions in the manufacturing and process industries, with an initial focus on semiconductor manufacturing, oil and gas processing, and energy. It is the latest venture by Jon Herlocker, a serial entrepreneur and deep technologist with a track record of founding and building successful startups, as well as a former VP and CTO at VMware, a $12 billion dollar a year virtual infrastructure management company. Tignis is funded and advised by industry leaders. Software executive Paul Maritz is an investor and member of Tignis’ Board of Directors. Maritz was CEO at VMWare, and at Microsoft he was a member of the top executive management team. Harel Kodesh, former CTO of GE Digital, is also an investor.

“The PAICe product suite puts Machine Learning in the hands of people that have never been able to use it before,” said Herlocker. “This is important because Machine Learning-based control algorithms not only outperform classic feedback or feedforward Advanced Process Control, they continuously learn from new process data reducing the need to retune controls and improve over time. With the PAICe product suite, many more manufacturers will now be able to take advantage of the benefits of machine learning in modern manufacturing and process control by increasing process quality, throughput and yield.”

The PAICe product suite enables Machine Learning for more than just predictive maintenance – it enables it for process optimization and directly in process control loops. It is able to run Machine Learning-based simulations one million times faster than legacy physics-based simulations, allowing manufacturers to have real time feedback control in places that were not possible in the past such as real-time optimization. Key features of the suite include:

PAICe Builder, a Machine Learning analytics tool easy enough for anyone to use. It provides simple connectivity to OSIsoft PI data historian and other data sources, and is available in both downloadable or cloud versions, allowing you to do analytics anywhere.

PAICe Monitor, which allows you to easily deploy your analytics to private or public cloud infrastructure and thousands of assets with one click (including Web APIs to ingest and send data to and from data historians). It offers a scalable cloud infrastructure so you can build the analytics you need, and the Tignis managed infrastructure means you only pay for the resources you need.

PAICe Maker, which deploys and manages machine learning based control algorithms that improve over time with more data. Machine learning models can compute control variables at speed one million times faster than legacy physics-based simulations, allowing real time computation of control. Hybrid on-premise and cloud architecture ensures low latency for control but the best possible model training and learning in the cloud.

Through the company’s extensive beta test program prior to launch, the PAICe product suite is in use by a number industrial clients spanning the Oil and Gas, Semiconductor and Energy industries. Some notable users of the product suite include Tokyo Electron (TEL), Synopsys, Etairon, and Optimum Energy. The product suite is now available directly from Tignis.

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Why Tignis Engineers Love Our Applications

Antoinette McKinley — Thu, 25 Feb 2021 18:52:54 +0000

PHYSICS, MACHINES AND DATA

Why Tignis Engineers Love Our Applications and Why You Should Try Them

25. February 2021|inTignis News

As a Tignis team member, I can say I am always excited and surprised with the tools put together by our team. Internally, these tools have helped us deliver incident reports to our customers to save them time by quickly guiding their attention to incidents of interest. One such tool, Tignis’s interactive data analysis application powered by DTQL, has become so compelling that it now stands out in its own right. DTQL stands for Digital Twin Query Language because it leverages the interaction between physical components or assets in a system or process and then navigates relationships to strengthen analysis techniques.

The exciting news for you is that we are releasing this previously internal-only functionality for use by any team looking to get advanced analytical value out the sensor data from their physical equipment. We are already seeing strong interest from automation teams and maintenance teams. Tignis’s analysis application powered by DTQL is a line-by-line interpreter and graphing explorer that allows rules to be declared using logic, mathematical, statistical, or ML functions, the results of which can be plotted or used to create highlighted portions of interest. I have had a very pleasant and illuminating experience with this tool, including continuous surprises with its capabilities, and amazement at the anomalies it picks up that would have otherwise escaped me. I think this application would be of immediate help to many people. Allow me to tell you why I love it so much.

Currently, many maintenance teams are using Excel as a tool for data analysis. Don’t get me wrong, Excel has its place in the world and is an impressive tool in its own right. However, for data analysis it can be a bit cumbersome: wait for the program to load all your data, make sure you have selected appropriate rows or columns, deal with missing or incorrect data, find and navigate to the plotting feature, ensure correctness of the settings used to plot your data, select more rows and columns, and visualize with graphs that have pre-configured time ranges with little ability to navigate within them or zoom in/out. Additionally, all of the preceding issues get repeated for every analysis you want to do, there are data limitations on top of this, and lastly data formatting issues can be experienced when you export to csv or during a data import. All of these issues go away with Tignis’s analysis application.

Tignis’s analysis application powered by DTQL aims to connect to existing infrastructure. The application was initially developed to be used exclusively by Tignis team members on Tignis’ platform, but we have also recently added an abstraction interface that allows us to create connectors to pull data in from other sources. Presently, the interpreter can pull data from CSV files and OSISoft Pi servers, but we plan to expand to more data sources in the future. Our aim here is to make it easy to work with your data. In the case of Pi, you can simply specify your server connection details and Tignis takes care of the rest.

Setting up a connection with Tignis’s application is extremely easy. Once the user provides their data historian credentials, it synchronizes data according to timestamp. There are no restrictions on sampling rate. Missing data is handled with flexibility. For example, the application makes it easy for you to extrapolate prior values to fill in gaps which is useful in the case where your system only stores changes in values. If you have sparse data, where some points are just missing, and you don’t know the correct value, a more sophisticated approach is to use ML to predict sensor values. This may sound complicated, but Tignis makes it so easy. I’m confident anyone can do it.

“If you have sparse data, where some points are just missing, and you don’t know the correct value, a more sophisticated approach is to use ML to predict sensor values. This may sound complicated, but Tignis makes it so easy. I’m confident anyone can do it.”

We have implemented the capability to work with templates. If you are familiar with OSI Pi’s element templates you will understand what this means. If not I’ll briefly explain: templates allow us to write rules on an asset class, which can then be applied to every individual entity (think asset) or element that fits within that template, saving a lot of time and effort. You can then select from a drop-down menu any individual entity that fits into that template to validate your rule against it. If you don’t have templates and only have a single entity or element you would like to work with, DTQL will accommodate that as well.

Tignis’ analytics application powered by DTQL has context-based intelligence baked in. If you are writing a rule on a template or entity and click the search button, it will pull up all the entities or templates available in your system. Once you have a target entity or template, clicking the search button again pulls up a list of sensors or features available to that target entity or template. In addition to the context based search feature, the search button also includes a list of all of the logic, mathematical, statistical, or ML functions that are available for use.

Now that I’ve mentioned why I personally love the new analysis application powered by DTQL from Tignis, let me tell you some of the things that you can do. I’ve had a lot of fun with it and I think you will too!

Create indicators and alerts for when scheduled maintenance should occur using equipment run times and meter readings (usage based maintenance)
Create alerts based on the number of occurrences of an event of interest using counting functionality.
Create alerts form SME knowledge using static thresholds for safety or reliability related events
Create multivariate physics based alerts from inputs such as equipment performance curves.
Create alerts using statistical analysis on signals where SME knowledge is not available
Create multivariate ML models for systems that are too complex or physics based equations are unknown.

All this allows the possibility to notify yourself or your team that you have a problem, after, during, or even before it has occurred.

I am excited to see our customers get as much out of this tool as we have internally. Tignis onboarded it’s first external users to the interactive data analysis application powered by DTQL this quarter and plans to release it more broadly over the coming months. If you’re interested in being an early adopter or trying the application, please visit info.tignis.com/dtql-app. Also, keep any eye on our website and social media for product release information!

Steven Herchak has worked at Tignis since January 2020. He is an Integration Engineer with a M.A.Sc. degree from the University of Victoria. Previously, Steven has worked with a building automation and controls company and Schneider Electric. At Tignis he is responsible for working directly with customers to interpret their needs and translate them into live analytics using DTQL.

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