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1. Toward mechanical proprioception in autonomously reconfigurable kirigami-inspired mechanical systems

   https://doi.org/10.1098/rsta.2024.0116  

   Jiao, Weijian; Shu, Hang; He, Qiguang; Raney, Jordan R (November 2024, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences)

   Mechanical metamaterials have recently been exploited as an interesting platform for information storing, retrieval and processing, analogous to electronic devices. In this work, we describe the design and fabrication a two-dimensional (2D) multistable metamaterial consisting of building blocks that can be switched between two distinct stable phases, and which are capable of storing binary information analogous to digital bits. By changing the spatial distribution of the phases, we can achieve a variety of different configurations and tunable mechanical properties (both static and dynamic). Moreover, we demonstrate the ability to determine the phase distribution via simple probing of the dynamic properties, to which we refer as mechanical proprioception. Finally, as a simple demonstration of feasibility, we illustrate a strategy for building autonomous kirigami systems that can receive inputs from their environment. This work could bring new insights for the design of mechanical metamaterials with information processing and computing functionalities. This article is part of the theme issue ‘Origami/Kirigami-inspired structures: from fundamentals to applications’. 

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2. Energy Efficient In-Memory Binary Deep Neural Network Accelerator with Dual-Mode SOT-MRAM

   https://doi.org/10.1109/ICCD.2017.107  

   Fan, Deliang; Angizi, Shaahin (November 2017, 2017 IEEE International Conference on Computer Design (ICCD))

   In this paper, we explore potentials of leveraging spin-based in-memory computing platform as an accelerator for Binary Convolutional Neural Networks (BCNN). Such platform can implement the dominant convolution computation based on presented Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) array. The proposed array architecture could simultaneously work as non-volatile memory and a reconfigurable in-memory logic (AND, OR) without add-on logic circuits to memory chip as in conventional logic-in-memory designs. The computed logic output could be also simply read out like a normal MRAM bit-cell using the shared memory peripheral circuits. We employ such intrinsic in-memory computing architecture to efficiently process data within memory to greatly reduce power hungry and omit long distance data communication concerning state-of-the-art BCNN hardware. 

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3. Autonomous Multistate Nanoencoding Using Combinatorial Ferroelectric Closure Domains in BiFeO ₃

   https://doi.org/10.1021/acsnano.5c07423  

   Checa, Marti; Millan-Solsona, Ruben; Liu, Yongtao; Pant, Bharat; Puretzky, Alexander; Cao, Ye; Kaur, Puneet; Yang, Jan-Chi; Collins, Liam; Domingo, Neus; et al (July 2025, ACS Nano)

   Recent advances in ferroic materials have identified topological defects as promising candidates for enabling additional functionalities in future electronic systems. The generation of stable and customizable polar topologies is needed to achieve multistates that enable beyond-binary device architectures. In this study, we show how to autonomously pattern on-demand highly tunable striped closure domains in pristine rhombohedral-phase BiFeO3 thin films through precise scanning of a biased atomic force microscopy tip along carefully designed paths. By employing this strategy, we generate and manipulate closed-loop structures with high spatial resolution in an automated manner, allowing the creation of highly tunable and intricate topological domain structures that exhibit distinct polarization configurations without the need for electrode deposition or complex heterostructure growth. As a proof-of-concept for ferroelectric beyond-binary memory devices, we use such topological domains as multistates, engineering an alphabet and automating the symbolic writing/reading process using autonomous microscopy. The resulting information density is compared with that of current commercially available memory devices, demonstrating the potential of ferroelectric topological domains for multistate information storage applications. 

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4. XOR-CiM: An Efficient Computing-in-SOT-MRAM Design for Binary Neural Network Acceleration

   https://doi.org/10.1109/ISQED57927.2023.10129322  

   Morsali, Mehrdad; Zhou, Ranyang; Tabrizchi, Sepehr; Roohi, Arman; Angizi, Shaahin (April 2023, 2023 24th International Symposium on Quality Electronic Design (ISQED))

   In this work, we leverage the uni-polar switching behavior of Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM) to develop an efficient digital Computing-in-Memory (CiM) platform named XOR-CiM. XOR-CiM converts typical MRAM sub-arrays to massively parallel computational cores with ultra-high bandwidth, greatly reducing energy consumption dealing with convolutional layers and accelerating X(N)OR-intensive Binary Neural Networks (BNNs) inference. With a similar inference accuracy to digital CiMs, XOR-CiM achieves ∼4.5× and 1.8× higher energy-efficiency and speed-up compared to the recent MRAM-based CiM platforms. 

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5. Skyrmion-mediated nonvolatile ternary memory

   https://doi.org/10.1038/s41598-024-66853-w  

   Rajib, Md Mahadi; Bindal, Namita; Raj, Ravish Kumar; Kaushik, Brajesh Kumar; Atulasimha, Jayasimha (July 2024, Scientific Reports)

   Multistate memory systems have the ability to store and process more data in the same physical space as binary memory systems, making them a potential alternative to existing binary memory systems. In the past, it has been demonstrated that voltage-controlled magnetic anisotropy (VCMA) based writing is highly energy-efficient compared to other writing methods used in non-volatile nano-magnetic binary memory systems. In this study, we introduce a new, VCMA-based and skyrmion-mediated non-volatile ternary memory system using a perpendicular magnetic tunnel junction (p-MTJ) in the presence of room temperature thermal perturbation. We have also shown that ternary states {− 1, 0, + 1} can be implemented with three magnetoresistance values obtained from a p-MTJ corresponding to ferromagnetic up, down, and skyrmion state, with 99% switching probability in the presence of room temperature thermal noise in an energy-efficient way, requiring ~ 2 fJ energy on an average for each switching operation. Additionally, we show that our proposed ternary memory demonstrates an improvement in area and energy by at least 2X and ~ 10e4 X respectively, compared to state-of-the-art spin-transfer torque (STT)-based non-volatile magnetic multistate memories. Furthermore, these three states can be potentially utilized for energy-efficient, high-density in-memory quantized deep neural network implementation. 

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