Edward Collins
I specialize in semiconductor back-end equipment and the manufacturing processes that turn wafers into tested and packaged semiconductor products. My research covers assembly, packaging, testing, inspection, burn-in, and the equipment infrastructure required to support high-volume semiconductor production. I write for this website because the back end of semiconductor manufacturing is becoming increasingly important as advanced packaging and heterogeneous integration become central to AI and high-performance computing. Packaging is no longer simply the final step after wafer fabrication; it increasingly influences bandwidth, power efficiency, thermal performance, and overall system design. My research follows assembly and test equipment, advanced packaging tools, inspection systems, burn-in equipment, and manufacturing capacity. I also examine the differences between traditional packaging and advanced approaches involving high-density interconnects, chiplets, and complex multi-die architectures. Another area of my work is equipment utilization and customer investment. Semiconductor manufacturers and outsourced assembly and test providers must balance capacity with rapidly changing demand. Equipment purchasing therefore reflects both current production requirements and expectations for future technology transitions. I also monitor the investment implications of advanced packaging and testing. Through my writing, I aim to explain how semiconductor packaging complexity is creating new equipment requirements and how these developments affect the broader semiconductor manufacturing ecosystem.
Back-end semiconductor equipment analyst covering assembly, testing, inspection, burn-in, advanced packaging tools, OSAT capacity, and equipment investment.
Edward Collins is a semiconductor equipment analyst specializing in back-end manufacturing, including assembly, packaging, testing, inspection, and burn-in. His research focuses on the equipment infrastructure required to transform fabricated wafers into qualified semiconductor products.
Edward follows the development of advanced packaging and its impact on equipment requirements. High-performance computing and AI systems increasingly rely on sophisticated multi-die integration, creating new requirements for assembly accuracy, inspection, testing, and thermal management.
His coverage includes traditional packaging as well as chiplet-based architectures, high-density interconnects, and other advanced integration approaches. Edward examines how increasing packaging complexity changes manufacturing processes and equipment intensity.
He also studies equipment utilization and capacity expansion among semiconductor manufacturers and outsourced assembly and test providers. Customer investment decisions can reveal expectations for future demand and technology adoption.
Through his articles, Edward provides a back-end perspective on the semiconductor equipment industry and explains how packaging, testing, inspection, and manufacturing capacity are evolving alongside advanced semiconductor architectures.
His Articles
The HBM Pre-Sell Phenomenon: How SK Hynix Sold Out 2026 Capacity Before Year-End
Memory chips have traditionally been a business of uncomfortable surprises. Demand accelerates, manufacturers expand, inventories accumulate, and prices eventually fall. The technology changes; the cycle often feels familiar.
Interpreting the Capital Market Signals of Samsung’s HBM3E Nvidia Certification
Samsung’s public announcement that its HBM3E modules achieved Nvidia certification is more than a product milestone—it sends multi-layered signals to capital markets about demand, competitive positioning, supply-chain dynamics, and future cash flows across memory makers, OSATs, accelerator OEMs, and equipment vendors.
Looking Ahead to 2027 – Product Matrix Outlook for AI Memory & Compute Thematic ETFs (Active/Passive/Leveraged)
The AI story has already split into layers. On one side, you have compute—GPUs, accelerators, ASICs, CPUs. On the other side, you have memory and storage—HBM, DRAM, NAND, SSDs, fabrics, persistence. In 2026, investors have finally started to treat memory as a theme in its own right, with first-generation memory ETFs and index derivatives turning the bottleneck into a tradable exposure. Looking ahead to 2027, it’s not hard to imagine a richer product landscape: a product matrix of AI memory and compute ETFs that come in different “styles” of intelligence and management.
The Logical Paradox of ESG-Screened AI Memory ETFs – Excluding High-Energy Memory Makers
AI memory is hot. ESG is hot. Put them together and you get something that looks clever on paper: an ESG-screened AI memory ETF, promising exposure to the hardware bottleneck that powers neural networks while still respecting environmental, social, and governance criteria. It sounds neat. It may even sound inevitable. But once you dig under the narrative, a paradox appears: the companies that dominate AI memory are also the ones consuming enormous amounts of energy and resources. Screening them for ESG while simultaneously making them the cornerstone of an AI storage and computing theme is not as simple as a checklist.