LAM Research Corporation: total assets
LAM Research total assets: $23.5 billion at 2026-06-28, up 10.2% on the year. 18 values since 2010, each linked to its SEC filing. Download the selected JSON.
All LAM Research Corporation financial histories
At a glance
LAM Research reported total assets of $23.5 billion at 2026-06-28, up 10.2% from $21.3 billion a year earlier. From 2010 to 2026 it grew at a compound 15.1% a year, from $2.49 billion to $23.5 billion. Across the last five reported years it ranged from $17.2 billion (2022-06-26) to $23.5 billion (2026-06-28).
Computed from the values in the table below, in USD; each links to the filing it came from.
What this measure means
Resources recognized on the balance sheet. Book assets are not the market value of the business.
Exact concept: us-gaap:Assets. Each value is a balance at the reporting date, not a flow earned over a year. Different units remain separate; no currency conversion or interpolation is applied.
Coverage of this history
Selected reporting periods run from 2010-06-27 to 2026-06-28. The SEC response was captured on 2026-09-19.
Selected filing history
| Period start | Period end | Value | Unit | Filed | Source filing |
|---|---|---|---|---|---|
| - | 2026-06-28 | 23,529,743,000 | USD | 2026-08-07 | 10-K · 0000707549-26-000037 SEC |
| - | 2025-06-29 | 21,345,260,000 | USD | 2026-08-07 | 10-K · 0000707549-26-000037 SEC |
| - | 2024-06-30 | 18,744,728,000 | USD | 2025-08-11 | 10-K · 0000707549-25-000075 SEC |
| - | 2023-06-25 | 18,781,643,000 | USD | 2024-08-29 | 10-K · 0000707549-24-000106 SEC |
| - | 2022-06-26 | 17,195,632,000 | USD | 2023-08-15 | 10-K · 0000707549-23-000102 SEC |
| - | 2021-06-27 | 15,892,152,000 | USD | 2022-08-24 | 10-K · 0000707549-22-000107 SEC |
| - | 2020-06-28 | 14,559,047,000 | USD | 2021-08-17 | 10-K · 0000707549-21-000136 SEC |
| - | 2019-06-30 | 12,001,333,000 | USD | 2020-08-18 | 10-K · 0000707549-20-000138 SEC |
| - | 2018-06-25 | 12,492,433,000 | USD | 2019-08-20 | 10-K · 0000707549-19-000124 SEC |
| - | 2018-06-24 | 12,479,478,000 | USD | 2019-08-20 | 10-K · 0000707549-19-000124 SEC |
| - | 2017-06-25 | 12,122,765,000 | USD | 2018-08-14 | 10-K · 0000707549-18-000115 SEC |
| - | 2016-06-26 | 12,264,315,000 | USD | 2017-08-15 | 10-K · 0000707549-17-000100 SEC |
| - | 2015-06-28 | 9,364,648,000 | USD | 2016-08-17 | 10-K · 0000707549-16-000050 SEC |
| - | 2014-06-29 | 7,993,306,000 | USD | 2015-08-13 | 10-K · 0001193125-15-290023 SEC |
| - | 2013-06-30 | 7,250,315,000 | USD | 2014-08-26 | 10-K · 0001193125-14-321759 SEC |
| - | 2012-06-24 | 8,004,652,000 | USD | 2013-08-27 | 10-K · 0001193125-13-348269 SEC |
| - | 2011-06-26 | 4,053,867,000 | USD | 2012-08-22 | 10-K · 0001193125-12-365446 SEC |
| - | 2010-06-27 | 2,487,392,000 | USD | 2011-08-19 | 10-K · 0001193125-11-227691 SEC |
Related LAM RESEARCH CORPORATION histories
Inspect the source
- Entity
- LAM RESEARCH CORPORATION / CIK 0000707549
- Captured
- SEC response SHA-256
5669585890b736938e937b858b2e62156a2cb4f3cb74845d8270eeee42689f90
Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON
Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.
Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.
Use this in research
A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.
These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.
Research methodology · Execution and cost assumptions · Check backtest overfitting
Build with the open-source tools
Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.
- Get an API key and run your first validation
- Connect the MCP server to your coding assistant
- Inspect the ALPHAC engine on GitHub
- Read the MCP server source and integration examples
The MCP server is free and MIT-licensed: npx -y canli-mcp. If these records help your research, a star on GitHub helps other researchers find them.
Read the published dataset with Python
import json
from urllib.request import urlopen
with urlopen("https://canlicapital.com/company-data/0000707549.json") as response:
record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
print(concept["tag"], next(iter(concept["observations"])))