Tag: AWS Semiconductor

Electronic Design Automation (EDA) tools are critical for designing silicon chips. In the cloud, pay-per-use mechanisms are the norm, but this traditionally isn’t the case for EDA licensing which can limit organizations’ ability to scale EDA on AWS. Synopsys Cloud Bring Your Own Cloud (BYOC) with FlexEDA is a groundbreaking solution that addresses this need. […]

Amazon is also a fabless semiconductor company. Several tapeouts are completed every year that result in products like Kindle, FireTV, Echo, etc. Amazon Web Services (AWS) does its inhouse semiconductor design for its data center operations through its internal team at Annapurna Labs. AWS Graviton, an ARM based CPU as well as AWS Trainium and […]

Manufacturing semiconductor devices using advanced nodes have many challenges, but perhaps none is more directly linked to the company’s profitability and competitive position than yield. Yield improvements directly reduce the cost of manufacturing a single die while also helping the company amortize the development costs across more dies. There are great commercial products helping customers […]

In 2021, NXP selected AWS as its preferred cloud partner to migrate the vast majority of its silicon design workloads from NXP data centers to AWS. The migration is well underway, and a number of teams have already taped out projects in their AWS cloud environment. In this blog post, we share more about NXP’s […]

Introduction Chip designs are increasing in complexity and size, which has resulted in additional transistors driving a need for greater processing power and memory. Greater silicon complexity makes physical verification with electronic design automation (EDA) applications more essential as any delays close to tape-out significantly impact production timelines. Silicon designers now require increased CPU and […]

Introduction Workbench testing is a key step in the manufacturing of semiconductor chips. Testing consists of sending electrical signals to semiconductor devices and comparing output signals to expected values. It is essential to ensure that the produced devices work according to specifications. Workbench testing is also time consuming and costly, so we do not want […]

Introduction Silicon design teams are constantly under pressure to deliver more efficient products faster, while keeping costs low. Electronic Design Automation (EDA) infrastructure teams, which provide compute, storage, and other IT resources to silicon design teams, can play a critical role in shrinking the RTL-to-GDSII cycle. Historically, compute capacity for EDA workloads has come from […]

Introduction As the global supply chain continues to impact the semiconductor industry, it is vital to find a better way to forecast demand and predict supply chain shortfalls. As a follow-on to my interview and content provided in Architecture Monthly: Semiconductor Design, this blog discusses the need for cross-industry data sharing and collaboration, and the […]

Introduction The high-volume manufacturing of semiconductor devices relies on complex wafer production and packaging processes, supported by a long list of specialized equipment manufacturers and materials suppliers. This heterogenous and globally distributed manufacturing supply chain must work closely with semiconductor design and verification teams. These teams create application-specific integrated circuits (ASICS) and systems-on-chip (SoC) to […]

Introduction This blog discusses the insights Ansys and AWS were able to gain by scaling Ansys RedHawk-SC on the AWS Cloud. The joint investigation outlines how to right-size AWS compute and storage resource for optimal price/performance results while running at scale. By combining the capabilities of Ansys RedHawk-SC with being able to scale compute resources […]