Maximizing Value Post-Moore’s Law


When Moore's Law was in full swing, almost every market segment considered moving to the next available node as a primary way to maximize value. But today, each major market segment is looking at different strategies that are more closely aligned with its individual needs. This diversity will end up causing both pain and opportunities in the supply chain. Chip developers must do more with a ... » read more

Moving Data And Computing Closer Together


The speed of processors has increased to the point where they often are no longer the performance bottleneck for many systems. It's now about data access. Moving data around costs both time and power, and developers are looking for ways to reduce the distances that data has to move. That means bringing data and memory nearer to each other. “Hard drives didn't have enough data flow to cr... » read more

Designing For Extreme Low Power


There are several techniques available for low power design, but whenever a nanowatt or picojoule matters, all available methods must be used. Some of the necessary techniques are different from those used for high-end designs. Others have been lost over time because their impact was considered too small, or not worth the additional design effort. But for devices that last a lifetime on a si... » read more

Power Impact At The Physical Layer Causes Downstream Effects


Data movement is rapidly emerging as one of the top design challenges, and it is being complicated by new chip architectures and physical effects caused by increasing density at advanced nodes and in multi-chip systems. Until the introduction of the latest revs of high-bandwidth memory, as well as GDDR6, memory was considered the next big bottleneck. But other compute bottlenecks have been e... » read more

Startup Funding: June 2020


Two Chinese startups drew big investment as the country aims to become more semiconductor independent as trade restrictions continue. One company deals in wafers, packaging, and IC design, while the other is focused on AI chips. Quantum computing startups didn't see large investments this month, as most are still very young companies, but the number of them grew with a new university spin-out e... » read more

Advanced Packaging Makes Testing More Complex


The limits of monolithic integration, together with advances in chip interconnect and packaging technologies, have spurred the growth of heterogeneous advanced packaging where multiple dies are co-packaged using 2.5D and 3D approaches. But this also raises complex test challenges, which are driving new standards and approaches to advanced-package testing. While many of the showstopper issues... » read more

Monitoring IC Abnormalities Before Failures


The rising complexities of semiconductor processes and design are driving an increasing use of on-chip monitors to support data analytics from an IC’s birth through its end of life — no matter how long that projected lifespan. Engineers have long used on-chip circuitry to assist with manufacturing test, silicon debug and failure analysis. Providing visibility and controllability of inter... » read more

Data Becomes Key For Next-Gen Chips


Data has become vital to understanding the useful life of a semiconductor — and the knowledge gleaned is key to staying competitive beyond Moore’s Law. What's changed is a growing reliance earlier in the design cycle on multiple sources of data, including some from further right in the design-through-manufacturing flow. While this holistic approach may seem logical enough, the semiconduc... » read more

Chip Reliability Vs. Cost


Semiconductor Engineering sat down to discuss the cost, reliability and security with Simon Segars, CEO of Arm; Joseph Sawicki, executive vice president of IC EDA at Mentor, a Siemens Business; Raik Brinkmann, CEO of OneSpin Solutions; Babak Taheri, CEO of Silvaco; John Kibarian, CEO of PDF Solutions; and Prakash Narain, CEO of Real Intent. What follows are excerpts of that virtual conversation... » read more

Are Better Machine Training Approaches Ahead?


We live in a time of unparalleled use of machine learning (ML), but it relies on one approach to training the models that are implemented in artificial neural networks (ANNs) — so named because they’re not neuromorphic. But other training approaches, some of which are more biomimetic than others, are being developed. The big question remains whether any of them will become commercially viab... » read more

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