Advanced Semiconductor Packaging: The Secret Hero for the AI Infrastructure Era
The Road to the AI Era is Paved in Semiconductor Manufacturing Innovation
This report provides insight to the force and speed of innovation required to propel artificial intelligence (AI), new requirements from across the computing landscape, and why foundational principles of semiconductor manufacturing are requiring re-invention to deliver the performance and scale of this new age. We cover the impact of generative AI and large language models (LLMs) across industries, current challenges in delivering performance to meet LLM requirements, how High Bandwidth Memory (HBM) has emerged as a foundational element of AI compute platforms, and a foreshadowing use case of upcoming chiplet-based processing solutions. We also look at how advanced 2.5D and 3D packaging, delivered in collaboration with market leader Lam Research, ensures the future of AI and continued semiconductor innovation.
Introduction
We’re seeing our world transform at warp speed, and the opportunities AI will unleash are just beginning to surface. What once seemed science fiction is actually closer than we may realize. OpenAI blew the doors off this arena with its recent demonstration of its Figure One robot performing complicated tasks and demonstrating complex decision processes. NVIDIA CEO Jensen Huang underscored this innovation, stating that robots will integrate across industries providing support for manual tasks and more.1 While robotics captures incredible inspiration for many of us, it is the tip of the iceberg for potential societal advancement in the years ahead. One of the most compelling areas for near-term benefit is pharmaceutical discovery. Here we’ve seen major tech players collaborating with traditional pharma and biotech startups to fuel a new generation of drug research that’s estimated to improve profitability in the sector by up to 25% according to McKinsey.2 What’s driving this investment? Bob Rogers, co-founder and Chief Scientific Advisor at leading healthcare AI startup BeeKeeperAI, explains,
“For each application area in drug development, the speedups reported by AI vendors are 3x to 10x. These accelerations by themselves are significant, but the real magic lies in the fact that every step of the drug development process is currently built from interlocking, inefficient human tasks. Replacement with AI tooling will result in wholesale reductions in the time it takes to propose, test, and report on new drugs in the market.”
Pharma transformation is being echoed across industries, and while each industry carries different near and far-term potential and will move at different speeds, it’s easy to conclude that entire industries will be transformed and our definition of work reshaped. In fact, we’re seeing the rapid evolution of a symbiotic relationship between humans and machines where AI can take on routine or tedious tasks, freeing humans to focus on innovation. This symbiosis even extends into the most complex invention undertaken by humans — the continued advancement of semiconductor manufacturing.
The Road to the AI Era is Paved in Semiconductor Manufacturing Innovation
This report provides insight to the force and speed of innovation required to propel artificial intelligence (AI), new requirements from across the computing landscape, and why foundational principles of semiconductor manufacturing are requiring re-invention to deliver the performance and scale of this new age. We cover the impact of generative AI and large language models (LLMs) across industries, current challenges in delivering performance to meet LLM requirements, how High Bandwidth Memory (HBM) has emerged as a foundational element of AI compute platforms, and a foreshadowing use case of upcoming chiplet-based processing solutions. We also look at how advanced 2.5D and 3D packaging, delivered in collaboration with market leader Lam Research, ensures the future of AI and continued semiconductor innovation.
Introduction
We’re seeing our world transform at warp speed, and the opportunities AI will unleash are just beginning to surface. What once seemed science fiction is actually closer than we may realize. OpenAI blew the doors off this arena with its recent demonstration of its Figure One robot performing complicated tasks and demonstrating complex decision processes. NVIDIA CEO Jensen Huang underscored this innovation, stating that robots will integrate across industries providing support for manual tasks and more.1 While robotics captures incredible inspiration for many of us, it is the tip of the iceberg for potential societal advancement in the years ahead. One of the most compelling areas for near-term benefit is pharmaceutical discovery. Here we’ve seen major tech players collaborating with traditional pharma and biotech startups to fuel a new generation of drug research that’s estimated to improve profitability in the sector by up to 25% according to McKinsey.2 What’s driving this investment? Bob Rogers, co-founder and Chief Scientific Advisor at leading healthcare AI startup BeeKeeperAI, explains,
“For each application area in drug development, the speedups reported by AI vendors are 3x to 10x. These accelerations by themselves are significant, but the real magic lies in the fact that every step of the drug development process is currently built from interlocking, inefficient human tasks. Replacement with AI tooling will result in wholesale reductions in the time it takes to propose, test, and report on new drugs in the market.”
Pharma transformation is being echoed across industries, and while each industry carries different near and far-term potential and will move at different speeds, it’s easy to conclude that entire industries will be transformed and our definition of work reshaped. In fact, we’re seeing the rapid evolution of a symbiotic relationship between humans and machines where AI can take on routine or tedious tasks, freeing humans to focus on innovation. This symbiosis even extends into the most complex invention undertaken by humans — the continued advancement of semiconductor manufacturing.
