Figure 1. Pathwave Advanced Design System (ADS) 2024 is engineered to enable multi-technology (RFIC, MMIC, antennas, packaging, PCBs, etc.) assembly and 3DEM-circuit-electro-thermal co-simulation for RF module design. New Python scripting automates and integrates ADS in multi-tool enterprise design flows.

PathWave Design 2024 for RF/Microwave Circuit and Module Design

  • Development of Keysight EDA solutions is driven by the fast-emerging trend of Artificial Intelligence (AI) and Machine Learning (ML) that requires massive amount of user generated data to be processed by powerful cloud-based compute farms to provide AI enabled service back to the user. 
  • Sending that massive amount of data via 5G or 6G wireless links requires operating frequencies to go up to mmWave and sub-THz 250 GHz. 
  • Design of circuits and modules for handling high data throughput at these frequencies involves the tight integration of multiple technologies, e.g., Si, III-V, packaging, antennas and PCB.
  • This high data throughput presents a discontinuity to RF/Microwave design workflows that necessitate heterogeneous integration of multiple tools to design these multi-technology products.
ADS 2024 - Heterogeneous Integration

Figure 2. ADS enables 3D integration of multi-technology (RFIC, MMIC, antennas, wafer level packaging, PCBs, etc.) into compact modules for 5G/6G and other mmWave applications which require accurate EM-circuit co-simulation of critical RF paths to interactively tune and optimize performance in the presence of unavoidable parasitics.

Multi-Technology Workflows for Heterogeneous Integration

  • Smart Mount and 3D Multi-Technology Physical Assembly Management
  • Module level physical verification LVS, LVL, DRC, ERC
  • Integrated Circuit, Electromagnetic, Electro-Thermal, System and Statistical analysis
  • Partnership with Ansys HFSS, Cadence, Synopsys and Siemens

Python Enabled Automation for Customizing Design Flows

  • Enables ADS to be optimally integrated into multi-tool Enterprise design flows
  • Adds user defined UI and custom functionality to ADS for design synthesis, data processing, instruments interface, AI/Machine Learning (ML) model training, etc.
  • Supports Visual Studio for Python code development and debugging
  • Partnership with foundries for mmWave design

Learn more about Python Automation

ADS 2024 - Python

Figure 3. Python scripting enables ADS to be automated, customized and integrated into design flows with other EDA tools for optimal deployment efficiency. Other examples include design synthesis, neural network model training and creation, instrument data acquisition and processing and UI customization.

ADS 2024 - MomGen2

Figure 4. Generation 2 Momentum 3D planar EM simulation offers 5x to 15x speedup. Intelligent mesh optimization around user selected nets within a large structure enables accurate efficient analysis without having to solve the entire structure. Large number of ports are handled with no speed penalty.

Faster, Higher Capacity 3DEM Simulation of Multi-Technology Integration

  • RFPro Momentum 5x to 15x speedup while preserving accuracy
  • Handles up to 600 ports and very large via arrays with no loading or runtime penalty
  • Intelligent mesh optimization around selected net from a large structure for efficient analysis

Learn more about RFPro

Cloud High Performance Computing (HPC) for Accelerating Electro-Thermal (ETH) Circuit Simulation

  • Keysight ETH simulation provides the industry’s highest spatial temperature resolution for the most accurate and reliable circuit analysis in tightly integrated 3D modules
  • Swept ETH circuit simulation can be parallelized to run on HPC with 10x typical speedup
  • Dynamic ETH model generation and reuse speeds up Transient and Envelope ETH circuit simulation by 10x to 100x
ADS 2024 - Electro-Thermal (ETH) High-Performance Computing (HPC)

Figure 5. Electro-Thermal (ETH) analysis significantly improves circuit simulation accuracy to guarantee electrical performance specs under transient or steady-state self-heating conditions and to avoid premature failures. New ETH Dynamic Model Generator and ETH high performance computing (HPC) acceleration improve simulation speed by 10x to 100x, making ETH circuit simulation an interactive design tool instead of a signoff verification tool.

ADS 2024 - Artificial Neural Network (ANN)

Figure 6. Python scripting opens the powerful ADS RF/Microwave design platform for adding custom capabilities, automation and integration. This example shows the creation of a nonlinear simulation model for use in ADS by training an artificial neural network (ANN) using measured or simulated graphical data with Python automation. It allows ADS to use this AI/ML technique to dramatically increase simulation coverage with new models.

AI Driven Linear and Nonlinear Circuit Modeling from Measured or Simulated Data

  • Use Python based Artificial Neural Network (ANN) modeling framework from Keysight proprietary DynaFET that is open for you to build your own nonlinear circuit simulation models
  • Train and create AI behavioral nonlinear models for circuit simulation from published datasheet, measured or simulated data
  • Ensure that you always have a valid simulation model
Circuit Design Software

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