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Managing Wafer Retest


Every wafer test touch-down requires a balance between a good electrical contact and preventing damage to the wafer and probe card. Done wrong, it can ruin a wafer and the customized probe card and result in poor yield, as well as failures in the field. Achieving this balance requires good wafer probing process procedures as well as monitoring of the resulting process parameters, much of it ... » read more

11 Ways To Reduce AI Energy Consumption


As the machine-learning industry evolves, the focus has expanded from merely solving the problem to solving the problem better. “Better” often has meant accuracy or speed, but as data-center energy budgets explode and machine learning moves to the edge, energy consumption has taken its place alongside accuracy and speed as a critical issue. There are a number of approaches to neural netw... » read more

Developers Turn To Analog For Neural Nets


Machine-learning (ML) solutions are proliferating across a wide variety of industries, but the overwhelming majority of the commercial implementations still rely on digital logic for their solution. With the exception of in-memory computing, analog solutions mostly have been restricted to universities and attempts at neuromorphic computing. However, that’s starting to change. “Everyon... » read more

Hunting For Open Defects In Advanced Packages


Catching all defects in chip packaging is becoming more difficult, requiring a mix of electrical tests, metrology screening, and various types of inspection. And the more critical the application for these chips, the greater the effort and the cost. Latent open defects continue to be the bane of test, quality, and reliability engineering. Open defects in packages occur at the chip-to-substra... » read more

Memory Issues For AI Edge Chips


Several companies are developing or ramping up AI chips for systems on the network edge, but vendors face a variety of challenges around process nodes and memory choices that can vary greatly from one application to the next. The network edge involves a class of products ranging from cars and drones to security cameras, smart speakers and even enterprise servers. All of these applications in... » read more

Complexity’s Impact On Security


Ben Levine, senior director of product management for Rambus’ Security Division, explains why security now depends on the growing number of components and the impact of interactions between those components. This is particularly problematic with AI chips, both on the training and inferencing side, where security problems on the training side can alter models for AI inferencing. » read more

Integrating Memristors For Neuromorphic Computing


Much of the current research on neuromorphic computing focuses on the use of non-volatile memory arrays as a compute-in-memory component for artificial neural networks (ANNs). By using Ohm’s Law to apply stored weights to incoming signals, and Kirchoff’s Laws to sum up the results, memristor arrays can accelerate the many multiply-accumulate steps in ANN algorithms. ANNs are being dep... » read more

3D Neuromorphic Architectures


Matrix multiplication is a critical operation in conventional neural networks. Each node of the network receives an input signal, multiplies it by some predetermined weight, and passes the result to the next layer of nodes. While the nature of the signal, the method used to determine the weights, and the desired result will all depend on the specific application, the computational task is simpl... » read more

Toward Neuromorphic Designs


Part one of this series considered the mechanisms of learning and memory in biological brains. Each neuron has many fibers, which connect to adjacent neurons at synapses. The concentration of ions such as potassium and calcium inside the cell is different from the concentration outside. The cellular membrane thus serves as a capacitor. When a stimulus is received, the neuron releases neur... » read more