In this Op-Ed, Dassault Systèmes' Manuel Rei says the semiconductor industry is facing immense opportunities but also major risks if it fails to adapt.

Semiconductors are the enabling technology behind nearly all modern technological advances. From smartphones to navigation systems and electric vehicle (EV) battery optimization, chips provide the processing power to drive innovation. The semiconductor industry is now evolving to meet exponentially increasing demands for better performance, lower power, and new capabilities. Unleashing the full potential of semiconductors will require embracing key trends like artificial intelligence (AI), managing complex global supply chains, and fostering partnerships based on open innovation.
Exponentially Rising Demand Drives Innovation
Demand for semiconductors is skyrocketing, driven by emerging technologies like 5G, Internet of Things (IoT), AI, EVs, and cloud computing. Each new smartphone contains billions of transistors. Data centers are filling up with custom AI chips to power machine learning (ML). The rise of IoT means chips being embedded into billions of smart devices. This exponential increase in demand is pushing chipmakers to deliver greater innovation and efficiency in design and manufacturing. Chips must pack more performance and functionality onto each unit of silicon. There is intense focus on advances like faster processing speeds, lower power consumption, and new architectures optimized for AI workloads. To keep pace, semiconductor firms need to leverage data, analytics, modeling, and simulation throughout the design process.

AI and Machine Learning Transform Chip Design
AI and ML are transforming semiconductor design and enabling major leaps in chip performance and capabilities. By utilizing large datasets and advanced algorithms, key aspects of chip creation canbe optimized in ways not possible manually. AI can help optimize architecture, physical layout, verification, and manufacturability. Chipmakers are developing specialized AI chips to power next-generation applications. But AI is also a design tool—virtual modeling combined with AI and ML can reduce errors, find insights humans can’t, and automate repetitive design tasks. This results in faster design cycles, improved performance, and lower costs. Specifically, AI-based simulations can model chip performance across a wide range of parameters and use the results to optimize the design. Reinforcement learning algorithms are being applied to incrementally tune chip layouts and architectures to maximize speed or energy efficiency. Supervised learning techniques can analyze millions of past designs to detect patterns and potential problems that engineers may miss.
Managing Complex Global Supply Chains
While driving innovation, semiconductor firms also face immense challenges managing their highly complex, global supply chains. The supply shortages and disruptions over the past few years have highlighted both supply chain fragility and the importance of resilience across these networks. There is now an increased focus on diversification to reduce over-reliance on single geographies for materials, manufacturing, or rare earth minerals. Firms also need much greater visibility and flexibility across their supply ecosystem. This requires real-time data sharing between customers, manufacturers, distributors, and hundreds of component suppliers across the globe. Digital tools like supply chain control towers can collect and analyze this data to enable rapid decision making. AI and ML will also play a key role in forecasting demand, optimizing production capacity, and strategically managing inventory buffers. Semiconductor companies need to digitally transform their supply chain operations to gain the agility, visibility, and resilience required to navigate future disruptions. Those that leverage technology to build robustness across their global production ecosystems will gain a significant competitive advantage.
Pursuing Sustainable Semiconductor Manufacturing
Semiconductor manufacturing has traditionally had a large environmental footprint due to its energy-intensive production processes and reliance on chemicals and water. Semiconductor firms now face increasing pressure from governments and investors to reduce emissions and mitigate climate impact. Strategies include switching to renewable energy sources, improving energy efficiency in fabrication facilities, and optimizing chip designs to reduce power consumption during use. Sustainability is becoming both an ethical imperative and competitive necessity. Companies that leverage data analytics to deeply understand and optimize their environmental impact can turn sustainability into an advantage. Collaborative efforts across the semiconductor ecosystem will also be key to developing green technology breakthroughs and establishing industry-wide emissions standards on the path to net zero.
Partnerships and Open Innovation Are Vital
Partnerships and open innovation ecosystems are becoming vital to semiconductor innovation. The complexity of modern chip design means no single firm can go it alone—there needs to be tight collaboration across foundries, tools providers, cloud partners, original equipment manufacturers (OEMs), and more. There is a major shift towards co-development partnerships between chipmakers and leading OEMs in segments like automotive, mobile devices, and industrial automation. Such partnerships allow the joint definition of chip requirements and integration of the devices into the OEM’s final products. Concurrently, open innovation models with IP licensing and sharing of early designs are emerging. For these collaborations to work, semiconductor firms need advanced virtual modeling capabilities to enable early collaboration and reduce costly errors downstream. Common data environments allow efficient design sharing across different teams and systems. New models like chiplet design, where modules are produced separately and integrated, will also require robust collaboration. With confidential IP to protect, building trust and security is essential. The firms that best embrace open innovation and create partner ecosystems will unlock immense creativity.
The recent CHIPS for America summit hosted by Purdue University convened industry, government, and academia to forge solutions to strengthen US semiconductor innovation and workforce development. Initiatives like these that facilitate collaboration and steer funding towards research, education, and training will be critical to advancing America’s leadership in semiconductors.
Looking to the Future
The semiconductor industry is facing immense opportunities as demand skyrockets, but also major risks if it fails to adapt. Companies must embrace emerging technologies like AI while managing global complexity, accelerating via partnerships, and focusing on sustainability. As 5G, IoT, cloud computing, and AI drive exponential demand growth, the chipmakers who embrace collaboration, digital transformation, and sustainable practices will lead the future. Adopting agile methodologies and leveraging partnerships and open innovation will allow greater speed and creativity. Pursuing environmental sustainability through efficiency gains, renewable energy, and optimized chip design will only grow more crucial. Unleashing the next era of semiconductor innovation while achieving climate goals will require a holistic focus. Companies must foster a collaborative culture across the global ecosystem while equipping it with digital capabilities and sustainable practices. The firms that bring together these diverse strengths will dominate the coming age of intelligent electronics and help build a cleaner and more secure world.
Manuel Rei is the Semiconductor Industry Solution Experience Director at Dassault Systèmes.