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Abstract 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 capturing 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.

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.

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 compute in- memory (CiM) is a promising alternative to room temperature von-Neumann technologies since cryogenic electronics can provide high speed and energy efficiency while CiM can solve the von-Neumann and memory wall bottlenecks. Moreover, CiM can reduce the energy requirement which can solve the cooling issues associated with the cryogenic systems. Here, we demonstrate a cryogenic in- memory full adder using Majority logic implemented in quantum anomalous Hall effect (QAHE)-based memory system. The utilization of QAHE-based memory enables a number of unique advantages for Majority logic– (i) Majority outputs correspond to voltages with two opposite polarities irrespective of the number of inputs and hence, only a simple voltage comparator can be used, (ii) in- memory Majority logic (and hence, full adder) does not require any modification in the peripheral circuitry, and (iii) the topologically protected Hall resistance states provide robustness against external variations. This work makes the first attempt to harness the unique advantages of QAHE phenomenon in implementing complex operations beyond the primitive bitwise Boolean operations.

Abstract Realizing compact and scalable Ising machines that are compatible with CMOS-process technology is crucial to the effectiveness and practicality of using such hardware platforms for accelerating computationally intractable problems. Besides the need for realizing compact Ising spins, the implementation of the coupling network, which describes the spin interaction, is also a potential bottleneck in the scalability of such platforms. Therefore, in this work, we propose an Ising machine platform that exploits the novel behavior of compact bi-stable CMOS-latches (cross-coupled inverters) as classical Ising spins interacting through highly scalable and CMOS-process compatible ferroelectric-HfO2-based Ferroelectric FETs (FeFETs) which act as coupling elements. We experimentally demonstrate the prototype building blocks of this system, and evaluate the scaling behavior of the system using simulations. Our work not only provides a pathway to realizing CMOS-compatible designs but also to overcoming their scaling challenges.

Abstract Ferroelectrics offer a promising material platform to realize energy-efficient non-volatile memory technology with the FeFET-based implementations being one of the most area-efficient ferroelectric memory architectures. However, the FeFET operation entails a fundamental trade-off between the read and the program operations. To overcome this trade-off, we propose in this work, a novel device concept, Mott-FeFET, that aims to replace the Silicon channel of the FeFET with VO2- a material that exhibits an electrically driven insulator–metal phase transition. The Mott-FeFET design, which demonstrates a (ferroelectric) polarization-dependent threshold voltage, enables the read current distinguishability (i.e., the ratio of current sensed when the Mott-FeFET is in state 1 and 0, respectively) to be independent of the program voltage. This enables the device to be programmed at low voltages without affecting the ability to sense/read the state of the device. Our work provides a pathway to realize low-voltage and energy-efficient non-volatile memory solutions.

The predictive capability of existing physical descriptions of multi-state devices (e.g., oxide memristors, ferroelectrics, antiferroelectric, etc.) cannot be fully leveraged in circuit simulations due to practical limitations regarding the complexity of compact models. We attempt to circumvent this issue by adopting a machine-learning (ML) - based approach to develop a compact model that retains the full physical description of these devices. ML-based modeling approaches have garnered immense interest in recent years and have already been successfully utilized in making models for several novel devices. A known hurdle for ML-based compact modeling is the need for a large amount of experimental data to properly train the model. We propose a method to simulate additional data by duplicating the data and adding Gaussian Noise to the duplicates. We propose a generalized framework to - (i) facilitate efficient training of ML-based device models, (ii) conduct seamless conversion to a Verilog-A model, and (iii) interface with industry-standard circuit simulators (HSPICE, SPECTRE, etc.). We demonstrate the capabilities of our framework using the hafnium oxide (HfOx) memristor as a test device. As the source of the training data, we use a physical model that unifies detailed atomic-level descriptions with self-consistent evaluation of electronic transport. In addition, we test our model with experimental data for multiple memristor samples and repeated cycles of the same sample. Our ML-based framework prepares a circuit-compatible compact model to facilitate system-level simulations. With our model, we achieve a root mean squared error (RMSE) of 0.000863 and an R2 of 0.977371 on our testing data.

Abstract The interplay between ferromagnetism and topological properties of electronic band structures leads to a precise quantization of Hall resistance without any external magnetic field. This so-called quantum anomalous Hall effect (QAHE) is born out of topological correlations, and is oblivious of low-sample quality. It was envisioned to lead towards dissipation-less and topologically protected electronics. However, no clear framework of how to design such an electronic device out of it exists. Here we construct an ultra-low power, non-volatile, cryogenic memory architecture leveraging the QAHE phenomenon. Our design promises orders of magnitude lower cell area compared with the state-of-the-art cryogenic memory technologies. We harness the fundamentally quantized Hall resistance levels in moiré graphene heterostructures to store non-volatile binary bits (1, 0). We perform the memory write operation through controlled hysteretic switching between the quantized Hall states, using nano-ampere level currents with opposite polarities. The non-destructive read operation is performed by sensing the polarity of the transverse Hall voltage using a separate pair of terminals. We custom design the memory architecture with a novel sensing mechanism to avoid accidental data corruption, ensure highest memory density and minimize array leakage power. Our design provides a pathway towards realizing topologically protected memory devices.

In neuromorphic computing, memristors (or “memory resistors”) have been primarily studied as key elements in artificial synapse implementations, where the memristor provides a variable weight with intrinsic long-term memory capabilities, based on its modifiable resistive-switching characteristics. Here, we demonstrate an efficient methodology for simulating resistive-switching of HfO2 memristors within Synopsys TCAD Sentaurus—a well established, versatile framework for electronic device simulation, visualization and modeling. Kinetic Monte Carlo is used to model the temporal dynamics of filament formation and rupture wherein additional band-to-trap electronic transitions are included to account for polaronic effects due to strong electron-lattice coupling in HfO2. The conductive filament is modeled as oxygen vacancies which behave as electron traps as opposed to ionized donors, consistent with recent experimental data showing p-type conductivity in HfOx films having high oxygen vacancy concentrations and ab-initio calculations showing the increased thermodynamic stability of neutral and charged oxygen vacancies under conditions of electron injection. Pulsed IV characteristics are obtained by inputting the dynamic state of the system—which consists of oxygen ions, unoccupied oxygen vacancies, and occupied oxygen vacancies at various positions—into Synopsis TCAD Sentaurus for quasi-static simulations. This allows direct visualization of filament electrostatics as well as the implementation of a nonlocal, trap-assisted-tunneling model to estimate current-voltage characteristics during switching. The model utilizes effective masses and work functions of the top and bottom electrodes as additional parameters influencing filament dynamics. Together, this approach can be used to provide valuable device- and circuit-level insight, such as forming voltage, resistance levels and success rates of programming operations, as we demonstrate.

III-nitride light-emitting devices have been subjects of intense research for the last several decades owing to the versatility of their applications for fundamental research, as well as their widespread commercial utilization. Nitride light-emitters in the form of light-emitting diodes (LEDs) and lasers have made remarkable progress in recent years, especially in the form of blue LEDs and lasers. However, to further extend the scope of these devices, both below and above the blue emission region of the electromagnetic spectrum, and also to expand their range of practical applications, a number of issues and challenges related to the growth of materials, device design, and fabrication need to be overcome. This review provides a detailed overview of nitride-based LEDs and lasers, starting from their early days of development to the present state-of-the-art light-emitting devices. Besides delineating the scientific and engineering milestones achieved in the path towards the development of the highly matured blue LEDs and lasers, this review provides a sketch of the prevailing challenges associated with the development of long-wavelength, as well as ultraviolet nitride LEDs and lasers. In addition to these, recent progress and future challenges related to the development of next-generation nitride emitters, which include exciton-polariton lasers, spin-LEDs and lasers, and nanostructured emitters based on nanowires and quantum dots, have also been elucidated in this review. The review concludes by touching on the more recent topic of hexagonal boron nitride-based light-emitting devices, which have already shown significant promise as deep ultraviolet and single-photon emitters.