Physics-inspired hardware solvers based on Ising machines have gained significant attention for addressing combinatorial optimization problems across diverse domains, including logistics, communication networks, and financial optimization. While the problems in these domains typically involve integer states, they are often reformulated to fit the quadratic unconstrained binary optimization (QUBO) framework supported by Ising machines. This is usually achieved by introducing penalty terms, which can degrade solution quality. In this work, we present a generalized integer-state probabilistic Ising machine (GIS-PIM) that natively supports integer-state encoding using compact binary representations. By avoiding one-hot encoding and associated constraints, GIS-PIM efficiently explores the solution space with fewer spin variables. The framework is implemented on a GPU and evaluated on publicly available graph coloring benchmarks, including both synthetic and realworld datasets. It demonstrates strong scalability, solving problem instances with up to 13 colors and graph sizes reaching approximately 20,000 nodes, a level not previously reported for other Ising-based methods. Our results indicate that the proposed approach: i) achieves solution accuracies exceeding 98%, competitive with leading classical methods such as Tabucol and graph neural network-based solver; and ii) significantly performs better over state-of-the-art simulated bifurcation (SBM) and QUBObased probabilistic Ising machines (Qu-PIM), achieving higher success probability and faster time-to-solution.

The emerging negative capacitance FinFET (NC-FinFET) device is a promising technology for the design of low-power VLSI circuits. This paper proposes ultra-low-power, highperformance, and low-area dynamic random access memory and sequential logic circuits based on NC-FinFETs. These circuits leverage the fact that NC-FinFETs have low leakage currents which help to facilitate a dynamic storage (DS)-based logic design style. This can in turn lead to reduced area overhead and help to compensate for higher delays that may be associated with NC-FinFETs. Our proposed circuit-level solutions can improve the data retention time of DS latch, flip-flop, and eDRAM circuits. Simulations with 14nm baseline FinFET (BS-FinFET) and NC-FinFET device models reveal that the proposed circuits offer up to 83.5% improvement in area-power-delay-product when compared to conventional BS-FinFET static-storage counterparts.

Gain cell embedded DRAMs (GC-eDRAM) are a potential alternative for conventional static random access memories thanks to their attractive advantages such as high density, low-leakage, and two-ported operation. As CMOS technology nodes scale down, the design of GC-eDRAM at deeply scaled nanometer nodes becomes more challenging. Deeply-scale technology nodes suffer from high leakage currents and result in low data retention times (DRTs) for GC-eDRAMs. Negative capacitance FinFETs (NC-FinFETs) are a promising emerging device for ultra-low-power VLSI design. Due to the lower leakage currents, NC-FinFETs can facilitate GC-eDRAM design with higher DRTs. We show that though NC-FinFETs have lower OFF currents and higher ION/IOFF ratios, their ON current is lower than FinFETs by approximately 30%, which results in lower performance. To benefit from the potential power efficiencies and the high DRTs of NC-FinFETs without sacrificing performance, we propose hybrid FinFET/NC-FinFET configurations for some prior 2T, 3T, and 4T GC-eDRAM cells. Simulations based on a 14nm experimentally calibrated NC-FinFET model suggest that the hybrid designs offer up to 96.8% and 86.3% improvements in DRT and static power consumption, respectively, when compared to the FinFET implementation. They also offer up to 47% read delay improvement over the NC-FinFET design. We also study the voltage scaling effects on DRT and refresh-energy of the proposed GC-eDRAM cells. The associated simulation results reveal that, with different supply voltages, the proposed hybrid 4T GC-eDRAM cell offers up to 370× less refresh-energy when compared to the other designs.

Since its inception, the semiconductor industry has used a physical dimension (the minimum gate length of a transistor) as a means to gauge continuous technology advancement. This metric is all but obsolete today. As a replacement, we propose a density metric, which aims to capture how advances in semiconductor device technologies enable system-level benefits. The proposed metric can be used to gauge advances in future generations of semi-conductor technologies in a holistic way, by accounting for the progress in logic, memory, and packaging/integration technologies simultaneously.

This month’s issue offers insight into efficient compression and execution of DNNs, the challenge of connecting rural areas, and the clique problem in wireless communication. which

In recent years, the negative capacitance effect in ferroelectric (FE) materials has attracted significant attention from many researchers around the world. The negative capacitance effect promises to reduce the voltage requirement in conventional complementary metal-oxide-semiconductor transistors below what is otherwise believed to be the Boltzmann limit. In this paper, our objective is to discuss the fundamental underpinning of the negative capacitance effect and describe how it can be utilized for transistors. We shall start with a thermodynamic perspective to understand where the reduction in energy dissipation comes from. We then proceed to derive the S curve in an FE material from fundamental principles. The central result of this paper is to associate the negative slope region in the S curve to a physically definable configuration of dipoles in the crystal structure. The design of a negative capacitance transistor is essentially an exercise of stabilizing the FE in the negative slope region of the S curve using the semiconductor capacitance as a series capacitor.

Conventional CMOS scaling and the Moore's law have been the cornerstone of progress in computing hardware technology. However, with dimensional scaling expected to end soon, there is a pressing need to find the next information processing hardware that can continue to support the technology revolution. Will this hardware solution be an enhanced or an augmented version of MOSFET or a switch based on a radically new switching mechanism. Ultimately, do we require a complete deviation from the Boolean paradigm itself? In this invited paper, we will review some of the actively pursued future logic, merged logic-memory and related concepts.

We study, for the first time, full chip power benefits of negative capacitance FET (NCFET) device technology for commercial-grade GDSII-level designs. Owing to sub-60mV/decade characteristics, NCFETs provide significantly higher drive-current than standard FETs at a given voltage, enabling significant iso-performance power savings by lowering VDD. We use SPICE models of NCFETs corresponding to 14nm node, which incorporate experimentally calibrated models of ferroelectric. We then characterize NCFET-based standard-cell libraries followed by full-chip NCFET-based GDSII-level design implementations of different benchmarks. Our results show that even with increased device capacitance, we can achieve about 4× (up to 74.7%) full-chip power reduction with low-VDD NCFETs over nominal VDD baseline FETs at iso-performance. The power savings are consistent across multiple benchmarks and are higher for low power designs.

Ferroelectric FETs (FEFETs) offer intriguing possibilities for the design of low power nonvolatile memories by virtue of their three-terminal structure coupled with the ability of the ferroelectric (FE) material to retain its polarization in the absence of an electric field. Utilizing the distinct features of FEFETs, we propose a 2-transistor (2T) FEFET-based nonvolatile memory with separate read and write paths. With proper co-design at the device, cell and array levels, the proposed design achieves non-destructive read and lower write power at iso-write speed compared to standard FERAM. In addition, the FEFET-based memory exhibits high distinguishability with six orders of magnitude difference in the read currents corresponding to the two states. Comparative analysis based on experimentally calibrated models shows significant improvement of access energy-delay. For example, at a fixed write time of 550ps, the write voltage and energy are 58.5% and 67.7% lower than FERAM, respectively. These benefits are achieved with 2.4 times the area overhead. Further exploration of the proposed FEFET memory in energy harvesting nonvolatile processors shows an average improvement of 27% in forward progress over FERAM.

Spin Transfer Torque RAM (STTRAM) is a promising candidate for fast, scalable, high-density, nonvolatile memory in nanometer technology. However, relatively high write current density and small volume of the memory device indicate the possibility of significant self-heating in the STTRAM structure. This article performs a critical analysis of the self-heating induced temperature variations in STTRAM. We perform a 3D finite volume method based study to characterize self-heating effect in a single cell. The analysis is extended for STTRAM arrays by developing a computationally efficient RC compact model based thermal analyzer. The analysis shows that self-heating can results in considerable increase in both steady-state value and transient change in temperature of individual cells. The effect is less pronounced at the array level and depends on the activity level, that is, number of active cells within an array size. The analysis further illustrates that self-heating negatively impacts electrical reliability metrics namely, read margin and detection accuracy; degrades cell performance; and modulates energy dissipation.

We present an accurate model of the self-heating effect in the Spin-Torque-Transfer RAM (STTRAM) using finite-volume-methods and thermal RC based compact models. We couple device level thermal simulation to the self-heating phenomenon to show that self-heating during write operation can result in significant temperature increase in STTRAM which in turn adversely affect the read disturb, leakage energy and sensing accuracy.

Spin-torque transfer magnetic RAM (STT MRAM) is a promising candidate for future embedded applications. It combines the desirable attributes of current memory technologies such as SRAM, DRAM, and flash memories (fast access time, low cost, high density, and non-volatility). It also solves the critical drawbacks of conventional MRAM technology: poor scalability and high write current. However, variations in process parameters can lead to a large number of cells to fail, severely affecting the yield of the memory array. In this paper, we analyzed and modeled the failure probabilities of STT MRAM cells due to parameter variations. Based on the model, we performed a thorough analysis of the impact of design parameters on parametric failures due to process variations. To achieve high memory yield without incurring expensive technology modification, we developed an efficient design paradigm from circuit and/or architecture perspective-to improve the robustness and integration density. The proposed technique effectively relaxes or completely decouples the conflicting design requirements for read stability, writability and cell area. It can be used at an early stage of the design cycle for yield enhancement.

Spin-Torque Transfer Magnetic RAM (STT MRAM) is a promising candidate for future universal memory. It combines the desirable attributes of current memory technologies such as SRAM, DRAM and flash memories. It also solves the key drawbacks of conventional MRAM technology: poor scalability and high write current. In this paper, we analyzed and modeled the failure probabilities of STT MRAM cells due to parameter variations. Based on the model, we developed an efficient simulation tool to capture the coupled electro/magnetic dynamics of spintronic device, leading to effective prediction for memory yield. We also developed a statistical optimization methodology to minimize the memory failure probability. The proposed methodology can be used at an early stage of the design cycle to enhance memory yield.