3D Integration Supports CIM Versatility And Accuracy

Compute-in-memory (CIM) is gaining attention due to its efficiency in limiting the movement of massive volumes of data, but it's not perfect. CIM modules can help reduce the cost of computation for AI workloads, and they can learn from the highly efficient approaches taken by biological brains. When it comes to versatility, scalability, and accuracy, however, significant tradeoffs are requir... » read more

28nm-HKMG-Based FeFET Devices For Synaptic Applications

A technical paper titled "28 nm high-k-metal gate ferroelectric field effect transistors based synapses- A comprehensive overview" was published by researchers at Fraunhofer-Institut für Photonische Mikrosysteme IPMS, Indian Institute of Technology Madras, and GlobalFoundries. Abstract This invited article we present a comprehensive overview of 28 nm high-k-metal gate-based ferroelectric f... » read more

Nonvolatile Capacitive Crossbar Array for In-Memory Computing

Abstract "Conventional resistive crossbar array for in-memory computing suffers from high static current/power, serious IR drop, and sneak paths. In contrast, the “capacitive” crossbar array that harnesses transient current and charge transfer is gaining attention as it 1) only consumes dynamic power, 2) has no DC sneak paths and avoids severe IR drop (thus, selector-free), and 3) can be f... » read more

Hybrid architecture based on two-dimensional memristor crossbar array and CMOS integrated circuit for edge computing

Abstract "The fabrication of integrated circuits (ICs) employing two-dimensional (2D) materials is a major goal of semiconductor industry for the next decade, as it may allow the extension of the Moore’s law, aids in in-memory computing and enables the fabrication of advanced devices beyond conventional complementary metal-oxide-semiconductor (CMOS) technology. However, most circuital demons... » read more

A crossbar array of magnetoresistive memory devices for in-memory computing

Samsung has demonstrated the world’s first in-memory computing technology based on MRAM. Samsung has a paper on the subject in Nature. This paper showcases Samsung’s effort to merge memory and system semiconductors for next-generation artificial intelligence (AI) chips. Abstract "Implementations of artificial neural networks that borrow analogue techniques could potentially offer low-po... » read more

Energy-efficient memcapacitor devices for neuromorphic computing

Abstract Data-intensive computing operations, such as training neural networks, are essential for applications in artificial intelligence but are energy intensive. One solution is to develop specialized hardware onto which neural networks can be directly mapped, and arrays of memristive devices can, for example, be trained to enable parallel multiply–accumulate operations. Here we show that ... » read more

Power/Performance Bits: Dec. 26

2nm memristors Researchers at the University of Massachusetts Amherst and Brookhaven National Laboratory built memristor crossbar arrays with a 2nm feature size and a single-layer density up to 4.5 terabits per square inch. The team says the arrays were built with foundry-compatible fabrication technologies. "This work will lead to high-density memristor arrays with low power consumption fo... » read more