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15.7 A 32Mb RRAM in a 12nm FinFet Technology with a 0.0249μm2 Bit-Cell, a 3.2GB/S Read Throughput, a 10KCycle Write Endurance and a 10-Year Retention at 105°C | IEEE Conference Publication | IEEE Xplore

15.7 A 32Mb RRAM in a 12nm FinFet Technology with a 0.0249μm2 Bit-Cell, a 3.2GB/S Read Throughput, a 10KCycle Write Endurance and a 10-Year Retention at 105°C


Abstract:

Low-power wireless MCU devices for intelligent IoT applications are one of the key drivers for embedded non-volatile memory (eNVM) for technology nodes of 2xnm and beyond...Show More

Abstract:

Low-power wireless MCU devices for intelligent IoT applications are one of the key drivers for embedded non-volatile memory (eNVM) for technology nodes of 2xnm and beyond; in addition to high-performance advanced CMOS processes with excellent RF/analog devices, there is a need for high read throughput high-density embedded non-volatile memory to store CPU code as well as neural-network models for energy-efficient data-centric machine-learning edge computing. For this purpose, fully logic-compatible TMO-based resistive RAM (RRAM) is a promising candidate [1–3]. In this work, a 32Mb RRAM macro with 0.0249 \mu m^{2} bit cells is implemented using a 12nm ultra-low power FinFET technology. Several design solutions are also proposed to address key challenges, including a write-assist scheme to achieve high write endurance and data retention and a pipeline-read scheme for high read throughput. Silicon measurements show 10,000 write-cycle endurance, 10-year retention at 105^{\circ}\mathrm{C}, and a 3.2GB/s read throughput.
Date of Conference: 18-22 February 2024
Date Added to IEEE Xplore: 13 March 2024
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ISSN Information:

Conference Location: San Francisco, CA, USA

Low-power wireless MCU devices for intelligent IoT applications are one of the key drivers for embedded non-volatile memory (eNVM) for technology nodes of 2xnm and beyond; in addition to high-performance advanced CMOS processes with excellent RF/analog devices, there is a need for high read throughput high-density embedded non-volatile memory to store CPU code as well as neural-network models for energy-efficient data-centric machine-learning edge computing. For this purpose, fully logic-compatible TMO-based resistive RAM (RRAM) is a promising candidate [1–3]. In this work, a 32Mb RRAM macro with bit cells is implemented using a 12nm ultra-low power FinFET technology. Several design solutions are also proposed to address key challenges, including a write-assist scheme to achieve high write endurance and data retention and a pipeline-read scheme for high read throughput. Silicon measurements show 10,000 write-cycle endurance, 10-year retention at , and a 3.2GB/s read throughput.

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