TSMC TSM
LRCX NASDAQ Large cap
How to buy Lam Research stock.
$332.25 -1.41% LRCX price as of August 15, 2026
Lam Research makes etch and deposition equipment critical to advanced semiconductor manufacturing. Its tools are used at both leading-edge nodes (for AI chips) and memory production (for HBM). Capacity additions at TSMC, Samsung, and Micron flow directly through Lam's order book.
- Market cap
- $421.71B
- P/E ratio
- 56.5
- Revenue (TTM)
- $23.23B
- Revenue growth (YoY)
- +30.0%
- 52-week range
- $94.11 – $438.50
- Day range
- $326.56 – $342.00
- Volume
- 7.42M
- Analyst target
- $368.94
What Lam Research does.
Lam Research specializes in the etch and deposition steps that build a chip's 3D structures layer by layer. It is especially strong in memory, the many-layered stacks of 3D NAND and the advanced DRAM/HBM that AI demands lean heavily on Lam's tools, while also serving leading-edge logic.
Because HBM and high-layer-count NAND are so etch- and deposition-intensive, Lam is one of the most memory-geared equipment names, so it benefits directly when AI drives memory capacity additions. Like Applied, it earns substantial recurring revenue servicing a large installed base. Its cycle follows overall WFE spending with a memory tilt.
Business segments
- Systems
Etch, deposition, and clean equipment.
- Customer Support / Spares
Service and upgrades on the installed base.
Key products
Plasma etch systemsDeposition (ALD/CVD) tools3D NAND & advanced-DRAM process tools
Why Lam Research is an AI stock.
Lam's equipment is used in the manufacturing steps that produce advanced AI chips and HBM memory. As AI drives increased semiconductor capital spending, Lam benefits alongside Applied Materials. HBM production in particular requires etch and deposition steps that Lam specializes in.
Lam Research is in the Foundries and chip equipment category. The fabs and lithography tools that make advanced AI silicon possible. Capacity here constrains the whole industry.
Lam Research in the AI ecosystem.
How Lam Research connects to the rest of the AI supply chain: the companies it buys from, sells to, partners with, and competes against.
Major customers
Competitors
- Applied Materials Overlapping etch/deposition.
Lam Research financials.
Key figures below are drawn from Lam Research's most recent reporting (latest quarter ending June 30, 2026). Market cap, revenue, and analyst target are shown in the stats above.
| EPS (diluted, TTM) | $5.97 |
|---|---|
| Forward P/E | 33.2 |
| Gross profit (TTM) | $11.73B |
| Profit margin | 31.3% |
| Operating margin | 37.4% |
| Dividend yield | 0.3% |
| Beta | 1.87 |
| Shares outstanding | 1.25B |
Fundamentals sourced from Alpha Vantage, last refreshed August 15, 2026. Figures are for information only, not investment advice.
How to buy Lam Research (LRCX) step by step.
- Choose a broker. Lam Research trades on the NASDAQ under the ticker LRCX. Any broker with access to NASDAQ can execute the trade.
- Open and fund an account. Most brokers take 5-15 minutes to open an account. You will need to verify your identity. Minimum investments vary by broker.
- Search for LRCX. Once funded, search for the ticker symbol LRCX in your broker's platform and confirm you are looking at NASDAQ.
- Place your order. Select the number of shares or a dollar amount. Many brokers offer fractional shares from as little as $1. Review any fees before confirming.
- Monitor your position. Lam Research's share price will move with broader market conditions and AI sector news. Use stop-loss orders if you want to limit downside.
Key facts.
| Ticker | LRCX |
|---|---|
| Exchange | NASDAQ |
| Category | Foundries and chip equipment |
| Market cap size | Large cap |
| Sector | TECHNOLOGY · SEMICONDUCTOR EQUIPMENT & MATERIALS |
| Founded | 1980 |
| Headquarters | Fremont, California |
| CEO | Tim Archer |
| Employees | ~17,000 |
| Fiscal year end | June |
Lam Research in the news
See all →Frequently asked questions.
Is Lam Research an AI stock?
Lam Research is an AI-adjacent stock. It earns from the equipment used to manufacture AI chips and HBM memory. The link is indirect but the correlation with AI hardware spending is high.