Efficient Verification of a RADAR SoC Using Formal and Simulation-Based Methods

TL;DR

This paper demonstrates a combined formal and simulation-based verification approach for a complex RADAR SoC, utilizing machine learning to enhance verification efficiency and ensure high-confidence sign-off.

Contribution

It introduces a requirements-driven verification flow that integrates formal, simulation, and ML-based methods for complex RADAR SoC validation.

Findings

Successful verification of a millimetre-accurate RADAR SoC

Effective use of ML to improve verification throughput

High-confidence verification sign-off achieved

Abstract

As the demand for Internet of Things (IoT) and Human-to-Machine Interaction (HMI) increases, modern System-on-Chips (SoCs) offering such solutions are becoming increasingly complex. This intricate design poses significant challenges for verification, particularly when time-to-market is a crucial factor for consumer electronics products. This paper presents a case study based on our work to verify a complex Radio Detection And Ranging (RADAR) based SoC that performs on-chip sensing of human motion with millimetre accuracy. We leverage both formal and simulation-based methods to complement each other and achieve verification sign-off with high confidence. While employing a requirements-driven flow approach, we demonstrate the use of different verification methods to cater to multiple requirements and highlight our know-how from the project. Additionally, we used Machine Learning (ML) based methods, specifically the Xcelium ML tool from Cadence, to improve verification throughput.