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Silicon Laboratories Inc.: research and development expense

Silicon Laboratories research and development expense: $353.2 million for the year ending 2026-01-03, up 6.3% on the year. 15 values since 2011.

All Silicon Laboratories Inc. financial histories

At a glance

Silicon Laboratories reported research and development expense of $353.2 million for the year ending 2026-01-03, up 6.3% from $332.2 million a year earlier. From 2011 to 2026 it grew at a compound 7.1% a year, from $136.0 million to $353.2 million. Across the last five reported years it ranged from $273.2 million (2022-01-01) to $353.2 million (2026-01-03). That was 45.0% of revenue ($784.8 million) for the same year.

Computed from the values in the table below, in USD; each links to the filing it came from.

What this measure means

Research and development costs recognized as expense. Capitalization policies and acquired projects can make this differ from total cash invested in development.

Exact concept: us-gaap:ResearchAndDevelopmentExpense. Each value covers an annual-duration reporting interval, shown with both start and end dates. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2011-01-02 to 2026-01-03. The SEC response was captured on 2026-09-20.

Selected filing history

Research and development expense in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2024-12-292026-01-03353,246,000USD2026-02-1010-K · 0001038074-26-000005 SEC
2023-12-312024-12-28332,225,000USD2026-02-1010-K · 0001038074-26-000005 SEC
2023-01-012023-12-30337,744,000USD2026-02-1010-K · 0001038074-26-000005 SEC
2022-01-022022-12-31332,326,000USD2025-02-0410-K · 0001038074-25-000003 SEC
2021-01-032022-01-01273,208,000USD2024-02-2010-K · 0001104659-24-025535 SEC
2019-12-292021-01-02235,185,000USD2023-02-0110-K · 0001104659-23-009254 SEC
2018-12-302019-12-28205,690,000USD2022-02-0210-K · 0001104659-22-010931 SEC
2017-12-312018-12-29238,347,000USD2021-02-0310-K · 0001104659-21-011613 SEC
2017-01-012017-12-30209,491,000USD2020-01-2910-K · 0001104659-20-007928 SEC
2016-01-032016-12-31199,744,000USD2019-01-3010-K · 0001047469-19-000300 SEC
2015-01-042016-01-02188,050,000USD2018-01-3110-K · 0001047469-18-000522 SEC
2013-12-292015-01-03172,985,000USD2017-02-0110-K · 0001047469-17-000461 SEC
2012-12-302013-12-28157,799,000USD2016-02-0510-K · 0001047469-16-010039 SEC
2012-01-012012-12-29137,952,000USD2015-02-0610-K · 0001047469-15-000641 SEC
2011-01-022011-12-31135,953,000USD2014-01-3110-K · 0001047469-14-000500 SEC

Related SILICON LABORATORIES INC. histories

All 65 Silicon Laboratories histories

Inspect the source

Entity
SILICON LABORATORIES INC. / CIK 0001038074
Captured
SEC response SHA-256
2813b8f8f0b70db706e91ca45f4293fad626b79097e532a5ca64634ec2559eab

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.

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/0001038074.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"])))