Characterization, Modeling, And Model Parameter Extraction Of 5nm FinFETs

A technical paper titled “A Comprehensive RF Characterization and Modeling Methodology for the 5nm Technology Node FinFETs” was published by researchers at IIT Kanpur, MaxLinear Inc., and University of California Berkeley. Abstract: "This paper aims to provide insights into the thermal, analog, and RF attributes, as well as a novel modeling methodology, for the FinFET at the industry stan... » read more

Accelerating Analog Design Migration

Today’s electronic chips are commonly comprised of a mix of analog, RF, and digital components, with increasing functionalities, complexities, and numbers of transistors reaching the trillions. While the digital side of the house can take advantage of automated design implementation tools, the analog world has always been more about doing things manually and in a very “custom” way—which... » read more

Use Tcl To Save Signals More Efficiently In AMS Simulations

Saving signal waveforms during a simulation is one of the basic ways to check the simulation results. However, with large SoC designs, it’s not always practical to save all signals during simulation, and the simulation performance might also be impacted by the number of signals being saved. Therefore, a crucial part of the simulation setup is to specify the expected and essential signals to s... » read more

Power Semis Usher In The Silicon Carbide Era

Silicon carbide production is ramping quickly, driven by end market demand in automotive and price parity with silicon. Many thousands of power semiconductor modules already are in use in electric vehicles for on-board charging, traction inversion, and DC-to-DC conversion. Today, most of those are fabricated using silicon-based IGBTs. A shift to silicon carbide-based MOSFETs doubles the powe... » read more

Analog Circuits Enabling Learning in Mixed-Signal Neuromorphic SNNs, With Tristate Stability and Weight Discretization Circuits

A technical paper titled “Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks” was published by researchers at University of Zurich and ETH Zurich. Abstract: "Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their spiking neural network circui... » read more

Analog IP Reuse

Analog integrated circuit IP is essential to how microelectronic circuits and systems interact with the environment. It enables things like signal conversion, stable power supply, and communication in state-of-the-art devices. However, designing these critical components – even though they are often a small part of complex chips – is very costly and risk-prone. And in today’s analog field... » read more

Comparing Analog and Digital SRAM In-Memory Computing Architectures (KU Leuven)

A technical paper titled "Benchmarking and modeling of analog and digital SRAM in-memory computing architectures" was published by researchers at KU Leuven. Abstract: "In-memory-computing is emerging as an efficient hardware paradigm for deep neural network accelerators at the edge, enabling to break the memory wall and exploit massive computational parallelism. Two design models have surge... » read more

Optimizing Projected PCM for Analog Computing-In-Memory Inferencing (IBM)

A new technical paper titled "Optimization of Projected Phase Change Memory for Analog In-Memory Computing Inference" was published by researchers at IBM Research. "A systematic study of the electrical properties-including resistance values, memory window, resistance drift, read noise, and their impact on the accuracy of large neural networks of various types and with tens of millions of wei... » read more

Performance Of Analog In-Memory Computing On Imaging Problems

A technical paper titled "Accelerating AI Using Next-Generation Hardware: Possibilities and Challenges With Analog In-Memory Computing" was published by researchers at Lund University and Ericsson Research. Abstract "Future generations of computing systems need to continue increasing processing speed and energy efficiency in order to meet the growing workload requirements under stringent en... » read more

Can Compute-In-Memory Bring New Benefits To Artificial Intelligence Inference?

Compute-in-memory (CIM) is not necessarily an Artificial Intelligence (AI) solution; rather, it is a memory management solution. CIM could bring advantages to AI processing by speeding up the multiplication operation at the heart of AI model execution. However, for that to be successful, an AI processing system would need to be explicitly architected to use CIM. The change would entail a shift ... » read more

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