Blog Review: November 1

Debug and UVM; system technology co-optimization in advanced packaging; battery management systems; noise modeling for quantum computing.


Cadence’s Rich Chang finds that although UVM has being used for testbench creation for more than a decade, it is still challenging to debug problems that are inside of UVM testbench.

Siemens’ Keith Felton suggests that early analysis in complex advanced packaging flows can enable designers to spot potential issues early to avoid built-in constructs that cause design failures and require major redesign work.

Synopsys’ Bryan Kelly and Marc Serughetti explores why battery management systems are necessary to oversee criteria and disciplines such as thermal input, electrical, hydraulic, and controls to ensure that the battery is optimized for performance, operates safely, and has a long lifespan.

Keysight’s Emily Yan delves into the importance of noise modeling for quantum computing and the challenges in low-frequency noise measurement.

Arm’s Parag Beeraka examines the cost and complexity of deploying IoT AI vision technology to capture consumer data inside retail stores and how the combination of improved and more-efficient processing at the edge, coupled with AI and machine learning, chips away at the roadblocks in front of many vision applications.

Ansys’ Mircea Popescu digs into electric motor testing, including some of the key tests, industry standards, and the role of simulation to reduce the number of design iterations and identify problems before any hardware is built.

SEMI’s Cassandra Melvin considers the role of MEMS and sensors in the XR and automotive markets along with the manufacturing breakthroughs enabling improved devices with higher yield and smaller footprint.

And don’t miss the blogs featured in the latest Systems & Design newsletter:

Technology Editor Brian Bailey cautions that understanding what can go wrong is even more essential when AI is involved.

Synopsys’ Yervant Zorian emphasizes the importance of thorough testing from die to system and how silicon lifecycle management helps complex designs work as intended.

Arteris’ Frank Schirrmeister explains how control and status register mismanagement can lead to expensive oversights.

Movellus’ Barry Pangrle points to two big technology advancements in quantum computing and what they mean.

Codasip’s Roddy Urquhart warns that memory safety vulnerabilities are a significant proportion of those reported and are growing in number.

Cadence’s Steve Brown shows how generative AI tools could boost design exploration and predictive analysis.

Siemens EDA’s John Ferguson calls for doing physical verification as early as possible in the design flow and continuing to check throughout the process.

Keysight’s Hwee Yng Yeo finds the route for meeting the needs of electrified trucks and buses starts at the battery cell chemistry level.

Expedera’s Paul Karazuba explains how training and inferencing at the edge enables AI applications with low latency, enhanced privacy, and the ability to function offline.

Leave a Reply

(Note: This name will be displayed publicly)