Cryogenic Memory Technologies

The surging interest in quantum computing, space electronics, and superconducting circuits has led to new developments in cryogenic data storage technology. Quantum computers promise to far extend our processing capabilities and may allow solving currently intractable computational challenges. Even with the advent of the quantum computing era, ultra-fast and energy-efficient classical computing systems are still in high demand. One of the classical platforms that can achieve this dream combination is superconducting single flux quantum (SFQ) electronics. A major roadblock towards implementing scalable quantum computers and practical SFQ circuits is the lack of suitable and compatible cryogenic memory that can operate at 4 Kelvin (or lower) temperature. Cryogenic memory is also critically important in space-based applications. A multitude of device technologies have already been explored to find suitable candidates for cryogenic data storage. Here, we review the existing and emerging variants of cryogenic memory technologies. To ensure an organized discussion, we categorize the family of cryogenic memory platforms into three types: superconducting, non-superconducting, and hybrid. We scrutinize the challenges associated with these technologies and discuss their future prospects.Comment: 21 pages, 6 figures, 1 tabl

CMOS-based Single-Cycle In-Memory XOR/XNOR

Big data applications are on the rise, and so is the number of data centers. The ever-increasing massive data pool needs to be periodically backed up in a secure environment. Moreover, a massive amount of securely backed-up data is required for training binary convolutional neural networks for image classification. XOR and XNOR operations are essential for large-scale data copy verification, encryption, and classification algorithms. The disproportionate speed of existing compute and memory units makes the von Neumann architecture inefficient to perform these Boolean operations. Compute-in-memory (CiM) has proved to be an optimum approach for such bulk computations. The existing CiM-based XOR/XNOR techniques either require multiple cycles for computing or add to the complexity of the fabrication process. Here, we propose a CMOS-based hardware topology for single-cycle in-memory XOR/XNOR operations. Our design provides at least 2 times improvement in the latency compared with other existing CMOS-compatible solutions. We verify the proposed system through circuit/system-level simulations and evaluate its robustness using a 5000-point Monte Carlo variation analysis. This all-CMOS design paves the way for practical implementation of CiM XOR/XNOR at scaled technology nodes.Comment: 12 pages, 6 figures, 1 tabl

Compact Model of a Topological Transistor

The precession of a ferromagnet leads to the injection of spin current and heat into an adjacent non-magnetic material. Besides, spin-orbit entanglement causes an additional charge current injection. Such a device has been recently proposed where a quantum-spin hall insulator (QSHI) in proximity to a ferromagnetic insulator (FI) and superconductor (SC) leads to the pumping of charge, spin, and heat. Here we build a circuit-compatible Verilog-A-based compact model for the QSHI-FI-SC device capable of generating two topologically robust modes enabling the device operation. Our model also captures the dependence on the ferromagnetic precision, drain voltage, and temperature with an excellent (> 99%) accuracy

Cryogenic Neuromorphic Hardware

The revolution in artificial intelligence (AI) brings up an enormous storage and data processing requirement. Large power consumption and hardware overhead have become the main challenges for building next-generation AI hardware. To mitigate this, Neuromorphic computing has drawn immense attention due to its excellent capability for data processing with very low power consumption. While relentless research has been underway for years to minimize the power consumption in neuromorphic hardware, we are still a long way off from reaching the energy efficiency of the human brain. Furthermore, design complexity and process variation hinder the large-scale implementation of current neuromorphic platforms. Recently, the concept of implementing neuromorphic computing systems in cryogenic temperature has garnered intense interest thanks to their excellent speed and power metric. Several cryogenic devices can be engineered to work as neuromorphic primitives with ultra-low demand for power. Here we comprehensively review the cryogenic neuromorphic hardware. We classify the existing cryogenic neuromorphic hardware into several hierarchical categories and sketch a comparative analysis based on key performance metrics. Our analysis concisely describes the operation of the associated circuit topology and outlines the advantages and challenges encountered by the state-of-the-art technology platforms. Finally, we provide insights to circumvent these challenges for the future progression of research

Superconducting Heater Cryotron-Based Reconfigurable Logic Towards Cryogenic IC Camouflaging

Superconducting electronics are among the most promising alternatives to conventional CMOS technology thanks to the ultra-fast speed and ultra-high energy efficiency of the superconducting devices. Having a cryogenic control processor is also a crucial requirement for scaling the existing quantum computers up to thousands of qubits. Despite showing outstanding speed and energy efficiency, Josephson junction-based circuits suffer from several challenges such as flux trapping leading to limited scalability, difficulty in driving high impedances, and so on. Three-terminal cryotron devices have been proposed to solve these issues which can drive high impedances (>100 k{\Omega}) and are free from any flux trapping issue. In this work, we develop a reconfigurable logic circuit using a heater cryotron (hTron). In conventional approaches, the number of devices to perform a logic operation typically increases with the number of inputs. However, here, we demonstrate a single hTron device-based logic circuit that can be reconfigured to perform 1-input copy and NOT, 2-input AND and OR, and 3-input majority logic operations by choosing suitable biasing conditions. Consequently, we can perform any processing task with a much smaller number of devices. Also, since we can perform different logic operations with the same circuit (same layout), we can develop a camouflaged system where all the logic gates will have the same layout. Therefore, this proposed circuit will ensure enhanced hardware security against reverse engineering attacks.Comment: 12 pages, 5 figure

Machine Learning-powered Compact Modeling of Stochastic Electronic Devices using Mixture Density Networks

The relentless pursuit of miniaturization and performance enhancement in electronic devices has led to a fundamental challenge in the field of circuit design and simulation: how to accurately account for the inherent stochastic nature of certain devices. While conventional deterministic models have served as indispensable tools for circuit designers, they fall short when it comes to capture the subtle yet critical variability exhibited by many electronic components. In this paper, we present an innovative approach that transcends the limitations of traditional modeling techniques by harnessing the power of machine learning, specifically Mixture Density Networks (MDNs), to faithfully represent and simulate the stochastic behavior of electronic devices. We demonstrate our approach to model heater cryotrons, where the model is able to capture the stochastic switching dynamics observed in the experiment. Our model shows 0.82% mean absolute error for switching probability. This paper marks a significant step forward in the quest for accurate and versatile compact models, poised to drive innovation in the realm of electronic circuits

Device-Circuit Co-Design Employing Phase Transition Materials for Low Power Electronics

Phase transition materials (PTM) have garnered immense interest in concurrent postCMOS electronics, due to their unique properties such as - electrically driven abrupt resistance switching, hysteresis, and high selectivity. The phase transitions can be attributed to diverse material-specific phenomena, including- correlated electrons, filamentary ion diffusion, and dimerization. In this research, we explore the application space for these materials through extensive device-circuit co-design and propose new ideas harnessing their unique electrical properties. The abrupt transitions and high selectivity of PTMs enable steep (\u3c 60 mV/decade) switching characteristics in Hyper-FET, a promising post-CMOS transistor. We explore device-circuit co-design methodology for Hyper-FET and identify the criterion for material down-selection. We evaluate the achievable voltage swing, energy-delay trade-off, and noise response for this novel device. In addition to the application in low power logic device, PTMs can actively facilitate non-volatile memory design. We propose a PTM augmented Spin Transfer Torque (STT) MRAM that utilizes selective phase transitions to boost the sense margin and stability of stored data, simultaneously. We show that such selective transitions can also be used to improve other MRAM designs with separate read/write paths, avoiding the possibility of read-write conflicts. Further, we analyze the application of PTMs as selectors in cross-point memories. We establish a general simulation framework for cross-point memory array with PTM based selector. We explore the biasing constraints, develop detailed design methodology, and deduce figures of merit for PTM selectors. We also develop a computationally efficient compact model to estimate the leakage through the sneak paths in a cross-point array. Subsequently, we present a new sense amplifier design utilizing PTM, which offers builtin tunable reference with low power and area demand. Finally, we show that the hysteretic characteristics of unipolar PTMs can be utilized to achieve highly efficient rectification. We validate the idea by demonstrating significant design improvements in a CockcroftWalton Multiplier, implemented with TS based rectifiers. We emphasize the need to explore other PTMs with high endurance, thermal stability, and faster switching to enable many more innovative applications in the future