William Brooks
I research global semiconductor ETFs with a focus on geographic diversification and regional industry exposure. My work examines how funds allocate across the United States, Asia, Europe, and other semiconductor markets. I write for this website because semiconductor production and innovation are distributed across multiple regions. A fund with a global mandate can therefore have very different exposure from one concentrated in a single market. My research compares geographic allocations, currency exposure, company concentration, regional semiconductor strengths, and index methodologies. I also examine how changes in regional market performance influence ETF portfolios. I follow funds that provide exposure to foundries, memory manufacturers, chip designers, equipment companies, and materials suppliers across different countries. Through my articles, I aim to help readers understand the geographic characteristics of semiconductor ETFs and the role that regional diversification plays in their portfolio structure.
Global semiconductor ETF analyst covering geographic allocation, regional exposure, currency effects, company concentration, and international semiconductor markets.
William Brooks is a global semiconductor ETF researcher specializing in geographic allocation and international industry exposure. His work examines how semiconductor funds distribute holdings across the United States, Asia, Europe, and other major markets.
William compares regional exposure to foundries, memory manufacturers, chip designers, equipment companies, and materials suppliers. He also studies how different index methodologies influence geographic concentration.
Currency exposure and regional market performance are important parts of his research. Changes in local equity markets and exchange rates can influence the effective exposure of globally diversified semiconductor funds.
Through his articles, William provides readers with a geographic perspective on semiconductor ETFs and explains how regional industry structures influence fund composition and performance.
His Articles
AI Chip Yield Challenges at 3nm: The Cost of Advanced Node Scaling
A smaller manufacturing node promises more computing capability within a constrained footprint. For AI chips, that can mean additional arithmetic units, improved energy efficiency, or room for functions that would be difficult to accommodate on an older process. The attraction is obvious. The economics are less automatic.
Overweight/Underweight Execution via Memory Thematic ETFs in Sector Rotation
Sector rotation in the age of AI is no longer just about “tech vs cyclicals” or “growth vs value.” It is increasingly about which layer of the AI stack you choose to emphasize at any given moment: compute, memory, storage, networking, or power. Among these, memory has quietly become one of the most powerful levers, because it sits at the bottleneck where AI performance and cost converge. Using memory-focused, or “memory semantic,” ETFs to execute overweight and underweight decisions inside broader sector rotation frameworks opens up a new, more nuanced way of moving capital between themes.
The Structurally Upward Shift in Memory ASP Driven by HBM
High Bandwidth Memory (HBM) has become a defining technology in modern computing architectures. As HBM adoption grows across GPUs, AI accelerators, and high-performance CPUs, it is doing more than just increasing raw throughput. HBM is driving a structural upward shift in the memory Application Service Profile (ASP)—the way systems expose, package, and price memory as part of broader computing platforms.
Liquidity Premium and Tracking Error of China Semi ETFs After AUM Exceeds 150B Yuan
China’s semiconductor ETFs have entered a stage where size itself becomes a market force. Once total assets under management cross a large threshold such as 150 billion yuan, the fund category stops behaving like a small collection of niche products and starts acting like an ecosystem. Liquidity improves, trading becomes more visible, market makers step in more aggressively, and the relationship between price, net asset value, and benchmark replication begins to change. That is where two ideas become especially important: the liquidity premium and tracking error.
Customer Concentration Risk in HBM: Over-Reliance on a Single AI Giant
High-Bandwidth Memory (HBM) sits at the heart of modern AI infrastructure. A small group of hyperscale cloud and AI platform providers now buys a very large share of global HBM output, often through multi‑year contracts and co‑investment arrangements. This buying power has helped finance HBM capacity expansion—but it has also created a new kind of risk for memory makers, OSATs, material suppliers and equipment vendors: extreme customer concentration.