Lam Research reported record quarterly revenue of $8.22 billion as artificial-intelligence infrastructure investment increased demand for advanced semiconductor manufacturing equipment. Revenue rose 22% from the preceding quarter and more than 54% from the corresponding period a year earlier.
GAAP net income reached $2.75 billion, equivalent to $2.19 per diluted share. This compared with $2.28 billion, or $1.81 per share, in the June quarter. Non-GAAP net income was $2.77 billion, with diluted earnings of $2.20 per share.
Gross margin rose to 52.3% from 51.7% in the previous quarter, while operating margin increased to 40% from 37.4%. On a non-GAAP basis, gross margin was 52.4% and operating margin reached 40.2%.
Chief executive Tim Archer said AI was driving higher semiconductor demand and increasing device complexity. Those trends are supporting investment in deposition, etch, cleaning and other manufacturing technologies required to produce advanced logic and memory chips.
Systems revenue totalled $5.73 billion, while customer-support and related revenue contributed $2.49 billion. The latter category includes services, replacement parts, upgrades and equipment for mature manufacturing processes.
China accounted for 35% of quarterly revenue, followed by South Korea at 20%, Taiwan at 18%, Japan at 9% and the United States at 8%. Southeast Asia generated 6% of revenue and Europe contributed 4%.
Cash, cash equivalents and restricted cash increased to $7.45 billion from $5.60 billion in the June quarter. Deferred revenue rose to $2.66 billion, excluding approximately $468.3 million of equipment shipped to Japanese customers but awaiting formal acceptance.
For the December quarter, Lam forecast revenue of $8.6 billion, plus or minus $400 million. It expects GAAP and non-GAAP gross margin of approximately 52.5%, operating margin of about 40.5% and diluted earnings of $2.33 per share, with a range of 15 cents on either side.
Lam’s performance reflects the expansion of semiconductor investment beyond accelerator design into the fabrication technologies required to manufacture increasingly complex AI processors and memory devices.
