SILICON LABORATORIES INC.: share-based compensation expense

What this measure means

Reported noncash expense for share-based payment arrangements. Noncash treatment does not mean the awards have no economic cost to shareholders.

Exact concept: us-gaap:ShareBasedCompensation. 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 2007-12-30 to 2026-01-03. The SEC response was captured on 2026-09-20.

Selected filing history

Related financial histories

• SILICON LABORATORIES INC.: total assets
• SILICON LABORATORIES INC.: total liabilities
• SILICON LABORATORIES INC.: stockholders equity
• SILICON LABORATORIES INC.: cash and cash equivalents
• SILICON LABORATORIES INC.: net income or loss
• SILICON LABORATORIES INC.: operating cash flow
• SILICON LABORATORIES INC.: capital expenditure payments
• SILICON LABORATORIES INC.: revenue
• SILICON LABORATORIES INC.: contract revenue excluding tax
• SILICON LABORATORIES INC.: financing cash flow
• SILICON LABORATORIES INC.: investing cash flow
• SILICON LABORATORIES INC.: retained earnings or deficit
• SILICON LABORATORIES INC.: basic weighted-average shares
• SILICON LABORATORIES INC.: diluted weighted-average shares
• SILICON LABORATORIES INC.: basic earnings per share
• SILICON LABORATORIES INC.: diluted earnings per share
• SILICON LABORATORIES INC.: income tax expense or benefit
• SILICON LABORATORIES INC.: net property, plant and equipment
• SILICON LABORATORIES INC.: operating income or loss
• SILICON LABORATORIES INC.: current assets
• SILICON LABORATORIES INC.: interest expense
• SILICON LABORATORIES INC.: current liabilities
• SILICON LABORATORIES INC.: current accounts payable
• SILICON LABORATORIES INC.: goodwill carrying amount
• SILICON LABORATORIES INC.: net current accounts receivable
• SILICON LABORATORIES INC.: common-stock repurchase payments
• SILICON LABORATORIES INC.: operating expenses
• SILICON LABORATORIES INC.: net inventory
• SILICON LABORATORIES INC.: gross profit
• SILICON LABORATORIES INC.: cost of revenue
• SILICON LABORATORIES INC.: selling, general and administrative expense
• SILICON LABORATORIES INC.: research and development expense

Inspect the source

Entity

SILICON LABORATORIES INC. / CIK 0001038074

Captured

2026-09-20T05:15:23.933Z

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.

• 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

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"])))