SRAM chips

Temperature-Dependent Soft-Error Analysis from 300K to 77K in Nanosheet and CFET

Temperature-Dependent Soft-Error Analysis from 300K to 77K in Nanosheet and CFET 150 150

Abstract:

We investigate the impact of cryogenic temperatures on the Single Event Upset (SEU) reliability of the Gate-All-Around (GAA) Nanosheet FET (NSFET) and Complementary FET (CFET) devices. At the device level, we assess heavy-ion radiation (HIR)-induced transients and carrier profiles and compare CFET and NSFET at temperatures from 400K to 77 …

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Nanoelectromechanically Gated Gain Cell eDRAM

Nanoelectromechanically Gated Gain Cell eDRAM 150 150

Abstract:

The memory wall and the slowdown of static random access memory (SRAM) scaling have renewed interest in gain-cell embedded dynamic random access memory (DRAM). While gain cells offer high density and energy efficiency, they suffer from limited retention, parasitic coupling, and the need for boosted write voltages. This work introduces …

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Topology-Aware Layout Design for Area-Decoupled Transistor Sizing in Multitier CFET SRAM

Topology-Aware Layout Design for Area-Decoupled Transistor Sizing in Multitier CFET SRAM 150 150

Abstract:

This work shows that a multitier complementary FET (CFET) static random access memory (SRAM) can decouple the area term from PPA-oriented transistor sizing. A topology-aware layout design is used to construct orthogonal and point-symmetric multitier CFET SRAM cells under 1-nm-class design rules, enabling high-density (HD), high-performance (HP), and high-current (HC) …

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3D-Integrated Near-Sensor AI for Energy-Constrained Environmental Monitoring Systems

3D-Integrated Near-Sensor AI for Energy-Constrained Environmental Monitoring Systems 150 150

Abstract:

Moving sensor data and neural-network operands through conventional external-memory hierarchies can constitute a substantial fraction of the energy required for edge inference. Motivated by a three-tier near-sensor organization, this work evaluates memory localization using a controlled architecture-level study and an independent transistor-level communication-link analysis. Five convolution configurations are evaluated on …

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An Eight-Channel Direct Analog Sensor Fusion Core for On-the-Spot Time-Series Analysis in Edge AI

An Eight-Channel Direct Analog Sensor Fusion Core for On-the-Spot Time-Series Analysis in Edge AI 150 150

Abstract:

A time-series analysis on multi-channel sensor signals is critical for wide-range Artificial Intelligence (AI) applications, such as in a field of mobility, robot, and infrastructure monitoring. Edge-device data collection followed by cloud-based analysis suffers from high latency and power consumption. This article presents an edge-AI-oriented sensor fusion architecture, performing analog …

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A 28-nm Sign-Bit-Embedded Bit-Parallel SRAM Compute-in-Memory Macro With Stage-Wise-Enabled Transition-Counting-Lines and Accumulators for Edge-AI Devices

A 28-nm Sign-Bit-Embedded Bit-Parallel SRAM Compute-in-Memory Macro With Stage-Wise-Enabled Transition-Counting-Lines and Accumulators for Edge-AI Devices 150 150

Abstract:

Computing-in-memory (CIM) architectures are promising for edge-AI devices, as they mitigate the von Neumann bottleneck and reduce data movement. Bit-parallel CIM macros with direct memory mapping and low toggle rate are well-suited for computing systems. However, severe routing congestion, inefficient signed computation, and unnecessary toggling of intermediate signals in local …

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STAR-SRAM: 16-bit Floating-Point SRAM-Based Digital Computing-in-Memory Macro in a 28 nm

STAR-SRAM: 16-bit Floating-Point SRAM-Based Digital Computing-in-Memory Macro in a 28 nm 150 150

Abstract:

A digital computing-in-memory (DCIM) macro emerges as a promising building block in a deep neural network (DNN) accelerator. To better support DNN workloads, circuit designers aim to improve three main metrics for macros: energy efficiency, compute density, and weight density. Improvements in those metrics directly translate into reduced energy consumption, …

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