Blog Review: May 10


Synopsys' Alessandra Nardi and Uyen Tran explain how to meet quality, reliability, functional safety, and security requirements of automotive chips through thorough test programs, path-margin monitoring, and design failure mode and effect analysis (DFMEA). Cadence's Veena Parthan explores how computational fluid dynamics can help predict and model the generation, propagation, and mitigation ... » read more

Designing Crash-Proof Autonomous Vehicles


Autonomous vehicles keep crashing into things, even though ADAS technology promises to make driving safer because machines can think and react faster than human drivers. Humans rely on seeing and hearing to assess driving conditions. When drivers detect objects in front of the vehicle, the automatic reaction is to slam on the brakes or swerve to avoid them. Quite often drivers cannot react q... » read more

Pinpointing Timing Delays Can Improve Chip Reliability


Growing pressure to improve IC reliability in safety- and mission-critical applications is fueling demand for custom automated test pattern generation (ATPG) to detect small timing delays, and for chip telemetry circuits that can assess timing margin over a chip's lifetime. Knowing the timing margin in signal paths has become an essential component in that reliability. Timing relationships a... » read more

3D Structures Challenge Wire Bond Inspection


Adding more layers in packages is making it difficult, and sometimes impossible, to inspect wire bonds that are deep within the different layers. Wire bonds may seem like old technology, but it remains the bonding approach of choice for a broad swath of applications. This is particularly evident in automotive, industrial, and many consumer applications, where the majority of chips are not de... » read more

ML Automotive Chip Design Takes Off


Machine learning is increasingly being deployed across a wide swath of chips and electronics in automobiles, both for improving reliability of standard parts and for the creation of extremely complex AI chips used in increasingly autonomous applications. On the design side, the majority of EDA tools today rely on reinforcement learning, a machine learning subset of AI that teaches a machine ... » read more

Hardware-Based Cybersecurity For Software-Defined Vehicles


As vehicle technology advances, so does the complexity of the electrical/electronic systems within these smart vehicles. A software-defined vehicle (SDV) relies on centralized compute and an advanced software stack to control most of its functionality, from engine performance to infotainment systems. SDVs are becoming more important as automakers look to improve vehicle performance, reduce emis... » read more

Role Of IoT Software Expanding


IoT software is becoming much more sophisticated and complex as vendors seek to optimize it for specific applications, and far more essential for vendors looking to deliver devices on-time and on-budget across multiple market segments. That complexity varies widely across the IoT. For example, the sensor monitoring for a simple sprinkler system is far different than the preventive maintenanc... » read more

Automotive Safety Island


The promise of autonomous vehicles is driving profound changes in the design and testing of automotive semiconductor parts. Automotive ICs, once deployed for simple functions like controlling windows, are now performing complex functions related to advanced driver-assist systems (ADAS) and autonomous driving applications. The processing power required results in very large and complex ICs that ... » read more

Blog Review: May 3


Synopsys' Thomas Andersen considers the requirements of AI-optimized chips that are resulting in exploration of different memory configurations, different types of memory, and different types of processor technologies and software components. Cadence's Girish Vaidyanathan considers the role of hierarchy and partitioning in custom design and looks at how a virtual hierarchy allows layout desi... » read more

AI Adoption Slow For Design Tools


A lot of excitement, and a fair amount of hype, surrounds what artificial intelligence (AI) can do for the EDA industry. But many challenges must be overcome before AI can start designing, verifying, and implementing chips for us. Should AI replace the algorithms in use today, or does it have a different role to play? At the end of the day, AI is a technique that has strengths and weaknesses... » read more

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