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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2505.14829 |
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| _version_ | 1866913939596509184 |
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| author | Lv, Yang Dixit, Brahmdutta Wang, Jian-Ping |
| author_facet | Lv, Yang Dixit, Brahmdutta Wang, Jian-Ping |
| contents | The conventional computer architecture has been facing challenges answering the ever-increasing demands from emerging applications, such as AI, for energy-efficient computation and memory hardware systems. Computational Random Access Memory (CRAM) represents a true in-memory computing paradigm that integrates logic and memory functions within the same array. At its core, CRAM relies on Magnetic Tunnel Junctions (MTJs), which serve as the foundational building blocks for implementing both memory storage and logic operations. However, a key challenge in CRAM lies in the non-ideal error rates associated with switching dynamics of MTJs, necessitating innovative approaches to reduce errors and optimize logic margins. This work proposes a novel approach of utilizing the voltage-controlled magnetic anisotropy (VCMA) to steepen the switching probability transfer curve (SPTC), thereby significantly reducing the logic operation error rate in CRAM. Using several numerical modeling tools, we validate the effectiveness of VCMA in modulating the energy barrier and switching dynamics in MTJs. It is revealed that the VCMA effect significantly reduces the error rate of CRAM by 61.43% at a VCMA coefficient of 200 fJ/V/m compared to CRAM without VCMA. The reduction of error rate is further rapidly amplified with an increasing TMR ratio. Furthermore, the introduction of the VCMA effect decreases the logic voltage (Vlogic) required for logic operations in CRAM and results in reduction of energy consumption. Our work serves as a first exploration in reducing the error rate in CRAM by modifying SPTC in MTJs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14829 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modulation of switching dynamics in magnetic tunnel junctions for low-error-rate computational random-access memory Lv, Yang Dixit, Brahmdutta Wang, Jian-Ping Emerging Technologies The conventional computer architecture has been facing challenges answering the ever-increasing demands from emerging applications, such as AI, for energy-efficient computation and memory hardware systems. Computational Random Access Memory (CRAM) represents a true in-memory computing paradigm that integrates logic and memory functions within the same array. At its core, CRAM relies on Magnetic Tunnel Junctions (MTJs), which serve as the foundational building blocks for implementing both memory storage and logic operations. However, a key challenge in CRAM lies in the non-ideal error rates associated with switching dynamics of MTJs, necessitating innovative approaches to reduce errors and optimize logic margins. This work proposes a novel approach of utilizing the voltage-controlled magnetic anisotropy (VCMA) to steepen the switching probability transfer curve (SPTC), thereby significantly reducing the logic operation error rate in CRAM. Using several numerical modeling tools, we validate the effectiveness of VCMA in modulating the energy barrier and switching dynamics in MTJs. It is revealed that the VCMA effect significantly reduces the error rate of CRAM by 61.43% at a VCMA coefficient of 200 fJ/V/m compared to CRAM without VCMA. The reduction of error rate is further rapidly amplified with an increasing TMR ratio. Furthermore, the introduction of the VCMA effect decreases the logic voltage (Vlogic) required for logic operations in CRAM and results in reduction of energy consumption. Our work serves as a first exploration in reducing the error rate in CRAM by modifying SPTC in MTJs. |
| title | Modulation of switching dynamics in magnetic tunnel junctions for low-error-rate computational random-access memory |
| topic | Emerging Technologies |
| url | https://arxiv.org/abs/2505.14829 |