Posted on Semiconductor Engineering: Click here to view original article
By: John Kibarian
The semiconductor industry stands at an inflection point. As Moore’s Law scaling becomes increasingly challenging and system complexity explodes through advanced packaging and chiplet-based architectures, the traditional siloed approach to manufacturing must give way to an unprecedented level of industry collaboration. This transformation, driven by the convergence of artificial intelligence, cloud analytics, and secure data sharing platforms, represents perhaps the most significant operational evolution since the foundry model.
The data foundation: Petabytes driving industry intelligence
Today’s semiconductor ecosystem generates staggering volumes of manufacturing data. At PDF Solutions alone, analytics systems now process petabytes of data on cloud, and much more on-premise, across equipment manufacturers, foundries, fabless companies, and IDMs. This represents hundreds of test chip tape-outs and analysis of production flows at memory, RF, logic, and AI system companies.
This data scale provides visibility into industry-wide trends and challenges. The breadth of touchpoints—from front-end fabrication through advanced packaging and test—enables anticipation of emerging needs years before they become critical. Such foresight is essential for R&D investments that may take five or more years to mature into customer solutions.
The collaboration imperative: From crisis response to operational integration
Historically, semiconductor industry collaboration emerged primarily during crises—when new technology nodes faltered or yield issues threatened product launches. Today’s market dynamics are fundamentally changing this pattern. Advanced packaging and chiplet integration are creating permanent collaboration requirements that extend far beyond traditional foundry-fabless relationships.
The explosion of test insertion points in multi-chiplet packages has increased both test complexity and costs. System-level testing now requires sophisticated robotics integration, while assembly tools demand increasingly tight process tolerances. This complexity necessitates ongoing collaboration spanning from system companies to equipment vendors, requiring new operational frameworks for sustainable partnerships.
Secure networks: The infrastructure for trusted data sharing
Today secureWISE, a secure, remote connectivity solution, connects 180 fab locations with over 100 equipment OEMs. The network’s growth trajectory suggests expansion across the entire value chain from equipment vendors to OSATs and fabs to fabless companies, encompassing the complete supply chain ecosystem. As AI adoption accelerates in production environments, such secure collaboration platforms become essential infrastructure.
Chiplets and the orchestration challenge
Chiplet-based architecture represents both the industry’s solution to Moore’s Law deceleration and its greatest operational challenge. While early chiplet systems primarily integrated components from single manufacturers, the future lies in multi-vendor component integration, requiring standards-based interoperability and sophisticated supply chain orchestration.
The production complexity is staggering: coordinating substrates, base dies, third-party components, various packaging technologies, OSAT configurations, and test systems across multiple suppliers. This orchestration must operate during initial bring-up and throughout ongoing production with the agility to reconfigure quickly for different product variants.
While there are precedents when the semiconductor industry came together to solve formidable engineering challenges, chiplet production at scale will require even deeper collaboration levels as system companies increasingly integrate components from diverse suppliers.
AI-driven operations: The path to exponential scale
The aspiration to reach trillion-dollar scale necessitates fundamental operational transformation. Human-in-the-loop processes that work for today’s volumes cannot scale to handle the complexity and velocity required for future growth.
Consider the data analytics challenge: human engineers examine only 5-10% of available manufacturing data. For companies producing millions of chips weekly, comprehensive human analysis is impossible. AI systems, however, can process this data comprehensively, identifying yield degradations, volume anomalies, and root causes automatically. Success requires AI-focused transformation by establishing governing principles for AI agents while removing human limitations at operational scale.
Platform architecture: Enabling ecosystem integration
The evolution toward platform-based collaboration demands a sophisticated technical architecture. Manufacturing platforms must integrate Enterprise Resource Planning (ERP) systems, Manufacturing Execution Systems (MES), Product Lifecycle Management (PLM), and real-time manufacturing data streams across organizational boundaries.
This integration depends on standardized data models and communication protocols. Industry standards bodies play crucial roles in enabling interoperability. The real value emerges when multiple participants adopt common platforms and data representations with shared analytics frameworks and standardized equipment interfaces.
The platform approach allows customers to adopt components selectively, using secure communications from one vendor, analytics from another, and equipment control from a third, while maintaining system-level integration through standards-based interfaces.
Implementation realities: Human governance with AI execution
Successful AI-driven collaboration requires careful balance between human oversight and autonomous operation. Humans establish the “bounding box,” defining collaboration principles, data-sharing parameters, security protocols, and operational limits. Within these constraints, AI agents handle day-to-day operations, processing massive data volumes and executing complex workflows without human intervention.
This governance model extends across organizational boundaries. Fabs define equipment vendor access policies, data transmission parameters, and software installation protocols. Once established, AI agents manage daily operations, handling the massive transaction volumes that would overwhelm human capacity.
The collaborative advantage
The semiconductor industry’s future belongs to companies that master collaborative, AI-driven operations. Technical challenges from advanced packaging complexity to chiplet supply chain orchestration require capabilities that no single company can develop in isolation. Success demands participation in secure, standards-based collaboration platforms that enable the industry’s collective intelligence while protecting individual competitive advantages.
Companies that establish these collaborative foundations early will be positioned to capture disproportionate value as the industry scales toward its trillion-dollar future. The transformation is not optional—it’s the prerequisite for competing in tomorrow’s semiconductor ecosystem.