Introducing Exensio® Aurora: PDF Solutions Unveils Highly-Scalable Architecture for Exensio Analytics

PDF Solutions launches Exensio Aurora, an AI-first, petabyte-scale analytics architecture for semiconductor manufacturing, with beta access starting September 2026.

Rhea-AI Summary

PDF Solutions (PDFS) has introduced Exensio Aurora, a new highly scalable architecture for its Exensio analytics platform, purpose-built to process semiconductor manufacturing data at petabyte scale and deploy agentic AI across operations and the supply chain.

The architecture emphasizes server-side analytics with a thin client, workflow-based usage, no-code analytics creation, and a natural-language interface. Key elements include a distributed engine designed for about 25X faster performance at comparable hardware cost, an AI-ready Manufacturing Data House, integrated model lifecycle management built on Kubernetes for edge deployment, LLM-enabled agents, and workflow-centric governance to reduce AI hallucinations. A beta release for early adopters is planned for September 2026, with first public demonstrations at PDF Solutions CONNECT 2026 on October 15–16 in San Francisco.

Market Context

On Aug 6, PDFS rose 7.73% after Q2 results cited Exensio customer wins; the Aurora unveiling extends...

Analysis

On Aug 6, PDFS rose 7.73% after Q2 results cited Exensio customer wins; the Aurora unveiling extends that platform area with a new architecture, but provides no financial outcome.

Key Figures

Performance Improvement: 25X Beta Release: September 2026 Public Demonstrations: October 15–16

Performance Improvement

25X

Designed distributed-engine performance at comparable hardware cost

Beta Release

September 2026

Planned availability for a small number of early adopters

Public Demonstrations

October 15–16

PDF Solutions CONNECT 2026 in San Francisco

Historical Context

1 past event · Latest: Aug 06

1 event

1. Aug 06

   Q2 earnings

   24h Move

   +7.7%

   Reported Exensio customer wins alongside higher quarterly revenue and reaffirmed 2026 growth target

24h Move is the share-price change in the day after each event; other market factors may also have contributed.

Key Terms

heterogeneous integration, osats, kubernetes, llm-enabled agents

4 terms

heterogeneous integration technical

"3D architecture, advanced packaging, and heterogeneous integration are driving innovation"

Heterogeneous integration is the practice of combining different types of electronic components — such as processors, memory, sensors and specialized chips — into a single package or tightly linked module, rather than building everything on one uniform chip. Like putting varied ingredients into one compact lunchbox to save space and improve function, it matters to investors because it can boost product performance, lower power use, shorten development time and create competitive or cost advantages that affect revenue and margins.

osats technical

"deployment to multiple edge locations like outsourced assembly and test houses (OSATs)"

Outsourced semiconductor assembly and test providers (OSATs) are companies that take finished silicon chips from chip makers and perform the packaging, final assembly and quality testing before the chips are shipped to device manufacturers. Think of them as specialized finishing factories that make sure fragile components are packaged and work reliably; their performance and capacity affect supply, costs and delivery times for the broader semiconductor industry, which can influence chipmakers’ margins and investors’ views on supply-chain risk.

kubernetes technical

"Built on Kubernetes, it is designed to enable central training"

Kubernetes is an open-source system that automates running and managing many pieces of software across groups of computers, like a conductor coordinating musicians so each piece plays at the right time and place. For investors, it matters because companies that use it can deploy updates faster, scale services up or down automatically, and cut infrastructure costs — factors that influence growth, reliability and operating margins.

llm-enabled agents technical

"customized LLM-enabled agents designed to assemble full workflows"

Software programs that combine large language models (LLMs) with tools, data access, and decision rules to carry out tasks with varying degrees of autonomy—such as drafting text, answering questions, automating workflows, or interacting with users and other systems. Like a smart assistant that can research, compose, and act, they matter to investors because they can change a company’s cost structure, productivity, product features, and regulatory exposures, affecting revenues, margins, and operational risk.

AI-generated analysis. How Rhea-AI works. Not financial advice.

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• Exensio^® Aurora technology is designed to handle semiconductor manufacturing data at petabyte scale and enable users to deploy agentic artificial intelligence (AI) with domain expertise across manufacturing operations and the supply chain
• Exensio^® Aurora leverages PDF Solutions' extensive 35-year experience to address the industry’s structural shifts

SANTA CLARA, Calif., Sept. 09, 2026 (GLOBE NEWSWIRE) -- PDF Solutions, Inc. (Nasdaq: PDFS), a leading provider of comprehensive data solutions for the semiconductor and electronics ecosystems, today unveils Exensio Aurora, the new, highly-scalable Exensio analytics architecture. Exensio Aurora is purpose-built to handle semiconductor manufacturing data at petabyte scale and to securely deploy agentic AI across manufacturing operations and the supply chain.

The first public demonstrations of the Exensio Aurora technology will take place at the PDF Solutions CONNECT 2026 event to be held on October 15–16 in San Francisco, CA. More information and registration for PDF Solutions CONNECT 2026 can be found at this link: https://events.pdf.com/connect2026/.

Scalable Architectures for Semiconductor Data Analytics

PDF Solutions’ Exensio Aurora addresses a structural shift in the semiconductor industry. Advancement in 3D architecture, advanced packaging, and heterogeneous integration are driving innovation and growth in the industry, dissolving the boundary between front-end and back-end and dispersing production globally. This complexity produces an explosion of data, yet in most cases, PDF’s experience is that less than 5% of semiconductor manufacturing data is analyzed with conventional tools.​

“The semiconductor industry thrives on innovation, which is fueled by collaboration,” remarked John Kibarian, PDF Solutions’ CEO, President, and co-founder. He continued, “The growth of 3D architecture and hybrid packaging is creating a larger and more complex global supply chain. Against this backdrop, delivering the operational efficiency that the industry needs requires a new kind of AI-driven collaboration, which can only be achieved when AI is applied at scale to align and analyze data from across the ecosystem, built for semiconductor.”

Commonly used business intelligence (BI) systems often run into limitations trying to process semiconductor manufacturing data that is both extremely long and extremely wide. Further, in many cases, general-purpose cloud data platforms lack the ability to integrate with manufacturing equipment and processes, do not include any industry-grounded semantic model, and do not have the ability to orchestrate actions across business applications and the supply chain.​ Given this combination of factors and at the urging of several of its customers, beginning more than a year ago, PDF Solutions chose to invest in a new kind of analytics architecture to power the PDF Solutions platform. It leverages PDF's extensive 35-year experience to address the industry’s structural shift.

“The challenge today is not just the volume of data that needs to be analyzed, but also the need to incorporate more automation, more machine learning, and more AI in that analysis,” stated Said Akar, PDF Solutions VP, Enterprise Development. He continued, “PDF Solutions’ strategy has always been to anticipate the needs of the semiconductor industry by incorporating in its solutions the latest technology advancement available. AI is the foundational guiding principle for the new Exensio Aurora architecture, designed to deliver unprecedented levels of scalability and new modes of user interaction with the application.”

Introducing Exensio Aurora

PDF Solutions provides a leading operations platform for the semiconductor manufacturing industry. The PDF Solutions Platform is differentiated from other offerings because it’s been designed to integrate four capabilities, essential to what the Company believes is effective management of semiconductor operations:

• PDF-proprietary equipment that generates unique, differentiated data to drive yield improvements;
• Connectivity and control of manufacturing equipment across fabs and remote locations;
• Orchestration of business processes across enterprise applications and the supply chain;
• Exensio, at the platform's core, to deliver advanced analytics and AI to semiconductor manufacturers, test floors, and system houses, that is powered by a semiconductor-specific ontology, a manufacturing data house, and an AI model development and deployment capability.

The new architecture of Exensio Aurora is purpose-built to deliver the speed and scale needed for both online and offline analytics, and to handle the data explosion driven by the growth of hybrid packaging and globally distributed manufacturing.

Exensio Aurora is not simply a faster version of the old model, however. It reflects a different design philosophy: server-side analytics with a lightweight client, a workflow-based platform, no-code creation of new analysis types, and a natural-language interface. Exensio Aurora is anchored on innovations regarding scalability, AI-readiness, and workflows.

• Scalable Analytics. The new Exensio Aurora distributed engine is designed to deliver approximately 25X faster performance at comparable hardware cost. PDF’s approach means that only the raw data needed for a given visualization is “brought” to the client, while pre-computed analytics are architected to keep pace with petabyte-scale systems. Data size is designed to be limited only by the analytics cluster, and performance is designed to scale linearly as volumes grow, in width or in length.
  

• Manufacturing Data House. This evolution of the Exensio data and semantic model is engineered to be AI-ready for data that is increasingly generated by logical operations rather than measured directly. It is designed to add enhanced support for 3D and higher-dimensional data, metadata for custom data models, and powerful search – each, a direct response to data that is growing more dimensional, more hierarchical, more geographically distributed, and increasingly “virtual.”
  

• Model Lifecycle. A single platform, Exensio Aurora is designed to enable users to create, manage, and govern models across their full lifecycle. This capability is tightly integrated with the Exensio database for training, simulation, and feed-forward deployment, and is architected to support bring-your-own-model workflows. Built on Kubernetes, it is designed to enable central training with deployment to multiple edge locations like outsourced assembly and test houses (OSATs), test floors, and edge boxes.
  

• Agentic AI. Grounded in three decades of semiconductor domain expertise, Exensio Aurora is architected with customized LLM-enabled agents designed to assemble full workflows, with on-premise options where cloud-hosted solutions are not preferred.
  

• AI-first interactivity. Exensio Aurora provides a standalone, thin-client UI with an integrated AI assistant to bring analytics to a wider set of users without the weight of legacy tooling.
  

• Workflows. Workflows are the language and long-term memory of Exensio Aurora: every analytic is a workflow (regardless of how it was created), rules are workflows, ML pipelines are workflows, and batch analytics are workflows. Workflows are designed to carry semiconductor-specific content and context at the block level, encode best-practice playbooks as flows, provide visibility into exactly how a result was achieved, and act as guardrails that ensure repeatability and prevent AI hallucination.
  

Availability

Exensio Aurora will first be available in September 2026 as part of a beta release program for a small number of early adopters. The Exensio Aurora technology September 2026 release will include technology from the following new offerings: Exensio Aurora Scalable Analytics, Exensio Aurora Scalable Data APIs, Exensio Aurora Manufacturing Data House, Exensio Aurora Automated Analytics, Exensio Aurora StudioAI, and Exensio Aurora Advanced Feed Forward.

Detailed demonstrations of these solutions will be provided during the PDF Solutions CONNECT event.

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, Section 21E of the Securities Exchange Act of 1934, as amended, and the “safe harbor” provisions of the Private Securities Litigation Reform Act of 1995. Other than statements of historical fact, all statements contained in this press release are forward-looking statements. These statements include, but are not limited to, statements related to the Company’s expectations regarding the Exensio Aurora architecture and its designed or anticipated capabilities, scalability, and performance, including expected performance improvements at comparable hardware cost; agentic AI functionality; the timing and scope of the September 2026 beta release; product development plans; potential customer and market acceptance; and other statements identified by words such as “could,” “expects,” “intends,” “may,” “plans,” “potential,” “should,” “will,” “would,” or similar expressions and the negatives of those terms, that are subject to future events and circumstances. Actual results could differ materially from those expressed in these forward-looking statements. Risks and uncertainties that could cause results to differ materially include risks associated with: the effectiveness of the Company’s business and technology strategies; semiconductor industry trends and competition; rates of adoption of the Company’s solutions by new and existing customers; project milestones or delays and performance criteria achieved; cost and schedule of new product development and investments in research and development; the continuing impact of macroeconomic conditions, including inflation, changing interest rates, and tariffs, energy prices, the evolving trade regulatory environment and geopolitical tensions, and other trends impacting the semiconductor industry, the Company’s customers, operations, and supply and demand for its products; supply chain disruptions; changes in laws and regulations, including recent tax and data privacy laws and regulations, or the interpretation or enforcement thereof; the success of the Company’s strategic growth opportunities and partnerships; recent and future acquisitions, strategic alliances and relationships and the Company’s ability to successfully integrate acquired businesses and technologies; whether the Company can successfully convert backlog into revenue; customers’ production volumes under contracts that provide Gainshare; the sufficiency of the Company’s cash resources and anticipated funds from operations; the Company’s ability to obtain additional financing if needed; the Company’s ability to use support and updates for certain open-source software; and other risks and uncertainties discussed in the Company’s periodic public filings with the Securities and Exchange Commission, including, without limitation, its Annual Report on Form 10-K for the year ended December 31, 2025, Quarterly Reports on Form 10-Q, and Current Reports on Form 8-K, and any amendments to such reports. All forward-looking statements made in this press release are made as of the date hereof, and, unless required by law, the Company undertakes no obligation to update publicly any such forward-looking statements.

About PDF Solutions

PDF Solutions (Nasdaq: PDFS) provides comprehensive data solutions designed to empower organizations across the semiconductor and electronics industry ecosystem to improve the yield and quality of their products and operational efficiency for increased profitability. The Company’s products and services are used by Fortune 500 companies across the semiconductor and electronics ecosystem to achieve smart manufacturing goals by connecting and controlling equipment, collecting data generated during manufacturing and test operations, and performing advanced analytics and machine learning to enable profitable, high-volume manufacturing.

Founded in 1991, PDF Solutions is headquartered in Santa Clara, California, with operations across North America, Europe, and Asia. The Company (directly or through one or more subsidiaries) is an active member of SEMI, INEMI, TPCA, IPC, the OPC Foundation, and DMDII. For the latest news and information about PDF Solutions or to find office locations, visit https://www.pdf.com.

PDF Solutions and the PDF Solutions logo are trademarks or registered trademarks of PDF Solutions, Inc. and/or its subsidiaries in the United States and other countries.

Company Contacts
Christophe Begue
VP, Corporate Strategic Marketing
christophe.begue@pdf.com

Sonia Segovia
Investor Relations
(408) 938-6491
sonia.segovia@pdf.com

FAQ

When will Exensio Aurora be available to users?

Exensio Aurora is planned to be available starting in September 2026 as part of a beta release program for a small number of early adopters.

What new offerings are included in the September 2026 Exensio Aurora release?

The September 2026 Exensio Aurora technology release will include the following offerings: Exensio Aurora Scalable Analytics, Exensio Aurora Scalable Data APIs, Exensio Aurora Manufacturing Data House, Exensio Aurora Automated Analytics, Exensio Aurora StudioAI, and Exensio Aurora Advanced Feed Forward.

Where and when will Exensio Aurora be demonstrated publicly for the first time?

The first public demonstrations of Exensio Aurora are scheduled for the PDF Solutions CONNECT 2026 event, to be held on October 15–16, 2026 in San Francisco, CA. More information and registration are available at https://events.pdf.com/connect2026/.