Impedance Measurement Handbook

Learn the basics of impedance measurements using Keysight LCR meters, impedance analyzers, and network analyzers. Explore impedance measurement techniques and tools.

Specifying Calibration Standards and Kits for Keysight Vector Network Analyzers

This application note covers calibration standard definitions, calibration kit content, and the structure requirements for Keysight's vector network analyzers.

Fundamentals of RF and Microwave Noise Figure Measurements

Explore information on RF and Microwave noise figure measurements, starting from the concept of noise figure and noise temperature, describing the noise characteristics of two-ports networks, and highlighting the different methods used to measure noise figure, such as Y-Factor and the Direct Noise measurement method.

Spectrum Analysis Basics (AN150)

Spectrum Analysis Basics teaches the fundamentals of spectrum analyzers and spectrum analysis including the latest advances in spectrum analyzer capabilities.

Time Domain Analysis Using a Network Analyzer

Time domain analysis is useful for impedance measurement and for evaluating a device problem. Learn how to apply the time domain display with network analyzers.

Advanced Design System (ADS) Circuit Design Cookbook 2.0

The ADS Circuit Design Cookbook provides getting started details of ADS software. The ADS circuit cookbook organizes the design work in the form of workspaces.

Keysight AI Data Center Fabric Test Methodology

The black book aims to prescribe a consistent and repeatable test process to provide measurable metrics with quantifiable KPIs (key performance indicator) that can be used to benchmark various implementations and ensure that data center operators optimize infrastructures for AI workloads.

De-Embedding and Embedding S-Parameter Networks Using a Vector Network Analyzer

Explore the techniques of de-embedding and embedding S-parameter networks with a DUT. The error coefficients can be modified using the error-correcting algorithms of the VNA.

Noise Figure Measurement Accuracy: The Y-Factor Method

This app note discusses the fundamentals of noise figure measurement using Y-factor method and how to avoid noise figure mistakes.

Measurement-Based Artificial Neural Network Simulation Models for RF Power Amplifiers

Applying Keysight's unique modeling tools and simulation engines to Power Amplifier modeling.

Basics of Measuring the Dielectric Properties of Materials

The dielectric properties covered in this app note are permittivity and permeability. Read more about the fundamentals of dielectric property measurements.

The Parametric Measurement Handbook - Edition 5

The 5th edition of Keysight’s Parametric Measurement Handbook is a complete parametric measurement reference, with information helpful for both novice and advanced users.

How to Make High Accuracy EVM Measurements for Power Amplifiers Using the Keysight Vector Network Analyzers

In this application note, you will learn an innovative method for characterizing nonlinearity of the power amplifier under modulated stimulus condition.

Basic Oscilloscope Fundamentals

The Power Handbook

Spectrum and Signal Analyzer Measurements and Noise

Noise is the classical limitation of electronics. In this application note, the characteristics of noise and its direct measurement are discussed in detail.

Solutions for Measuring Permittivity and Permeability with LCR Meters and Impedance Analyzers

This application note presents the technologies and methods for measuring permittivity and permeability. Learn more!

Fundamentals of RF and Microwave Power Measurements (Part 1)

Part 1 of fundamental power measurements covers understanding the basis for power measurements, sensor technologies and power meter theory.

Fundamentals of RF and Microwave Power Measurements (Part 3)

Part 3 of “Fundamentals of RF and Microwave Power Measurements” is a comprehensive overview of the contributing factors to power measurement uncertainty.

Instrument Control and Automation with Python

Easily control instruments and create test sequences with Python in a hybrid learning environment with Digital Learning Suite