arXiv subjects

Ghazi Sarwat Syed

Publications and source records attributed to Ghazi Sarwat Syed.

11 recordsLinked to original sources

arXiv2026

Electro-Thermal Origins of Enhanced Performance and Scalability in Disc-Type Phase-Change Memory

Disc-type phase-change memory is a promising device architecture for analog in-memory computing, enabled by the unique physical properties of ultra-thin phase-change materials and the device geometry. In this study, we develop a comparative analytical framework using compact electro-thermal models to examine the state dependent readout and programming behavior of these devices. The framework provides insight into the fundamental physical mechanisms responsible for their enhanced functionality and highlights promising directions for further device optimization and performance improvement.

cond-mat.mtrl-sci↗

arXiv2026

Analogue Phase Change Computational Memory with High Precision Reads and Energy Efficient Writes

Resistive memory technologies offer a compelling advantage for in-memory computing. However, realizing a device architecture that simultaneously achieves high computational precision, efficiency, and density has remained elusive due to inherent trade-offs among these performance metrics. Here, we introduce a com- pact phase-change memory device architecture that combines an ultra-confined active switching volume for enhanced electro-thermal efficiency with a non-insulating thin film that suppresses temporal conductance fluctuations. We analytically model and back-end integrate these devices into crossbar arrays. Even with conventional, undoped phase-change materials, the device architecture enables a computational precision approaching 6 bits, low conductance values below 50 μS with an adequate conductance window, and a viable pathway toward sub-100 μA programming currents under nominal operating voltages.

cond-mat.mtrl-sci↗

arXiv2026

Fully Parallel Multi-Agent Photonic Optimizer

Optimization problems are central to many important cross-disciplinary applications.In their conventional implementations, the sequential nature of operations imposes strict limitations on the computational efficiency. Here, we discuss how analog optical computing can overcome this fundamental bottleneck. We propose a photonic optimizer unit, together with supporting algorithms that uses in memory computation within a nature inspired, multi agent cooperative framework. The system performs a sequence of reconfigurable parallel matrix vector operations, enabled by the high bandwidth and multiplexing capabilities inherent to photonic circuits. This approach provides a pathway toward fast paced and high quality solutions for difficult optimization and search problems.

cond-mat.dis-nn↗

arXiv2026

Emerging Trends in Intelligent Sensing

The rapid proliferation of artificial intelligence, connected devices, and high speed mobile networks is driving unprecedented computational demands that challenge traditional sensor architectures. This article explores the shift toward edge computing, where computation is performed directly at the data source, and highlights the key architectures and performance metrics that may define the next generation of intelligent sensor systems.

cs.NI↗

arXiv2026

Probabilistic Photonic Computing

Probabilistic computing excels in approximating combinatorial problems and modelling uncertainty. However, using conventional deterministic hardware for probabilistic models is challenging: (pseudo) random number generation introduces computational overhead and additional data shuffling, which is particularly detrimental for safety-critical applications requiring low latency such as autonomous driving. Therefore, there is a pressing need for innovative probabilistic computing architectures that achieve low latencies with reasonable energy consumption. Physical computing offers a promising solution, as these systems do not rely on an abstract deterministic representation of data but directly encode the information in physical quantities. Therefore, they can be seamlessly integrated with physical entropy sources, enabling inherent probabilistic architectures. Photonic computing is a prominent variant due to the large available bandwidth, several orthogonal degrees of freedom for data encoding and optimal properties for in-memory computing and parallel data transfer. Here, we highlight key developments in physical photonic computing and photonic random number generation. We provide insights into the realization of probabilistic photonic processors and lend our perspective on their impact on AI systems and future challenges.

physics.app-ph↗

arXiv2025

A Case Study on the Performance Metrics of Integrated Photonic Computing

Photonic processors use optical signals for computation, leveraging the high bandwidth and low loss of optical links. While many approaches have been proposed, including in memory photonic circuits, most efforts have focused on the physical design of photonic components rather than full architectural integration with electronic peripheral circuitry. In this work, we present a microarchitecture level study that estimates the compute efficiency and density of three prominent photonic computing architectures. Our evaluation accounts for both the photonic circuits and the essential peripheral electronics required for optoelectronic and analog to digital conversions. We further demonstrate a heterogenous photonic electronic in memory processing approach for low latency neural network inference. These results provide a better understanding of the design aspects of photonic computing.

physics.optics↗

arXiv2025

Cryogenic In-Memory Computing with Phase-Change Memory

In-memory computing (IMC) is an emerging non-von Neumann paradigm that leverages the intrinsic physics of memory devices to perform computations directly within the memory array. Among the various candidates, phase-change memory (PCM) has emerged as a leading non-volatile technology, showing significant promise for IMC, particularly in deep learning acceleration. PCM-based IMC is also poised to play a pivotal role in cryogenic applications, including quantum computing and deep space electronics. In this work, we present a comprehensive characterization of PCM devices across temperatures down to 5 K, covering the range most relevant to these domains. We systematically investigate key physical mechanisms such as phase transitions and threshold switching that govern device programming at low temperatures. In addition, we study attributes including electrical transport, structural relaxation, and read noise, which critically affect readout behavior and, in turn, the precision achievable in computational tasks.

cond-mat.mes-hall↗

arXiv2025

A device-level compact model for mushroom-type phase change memory

In this work we introduce a compact model for mushroom-type phase-change memory devices that incorporates the shape and size of the amorphous mark under different programming conditions, and is applicable to both projecting and non-projecting devices. The model includes analytical equations for the amorphous and crystalline regions and uniquely features a current leakage path that injects current at the outer edge of the electrodes. The results demonstrate that accurately modeling the size and shape of the phase configurations is crucial for predicting the full-span of the RESET and SET programming, including the characteristics of threshold switching. Additionally, the model effectively captures read-out behaviors, including the dependence of resistance drift and bipolar current asymmetry behaviours on the phase configurations. The compact model is also provided in Verilog-A format, so it can be easily used in standard circuit-level simulation tools.

physics.app-ph↗

arXiv2025

In-Materia Speech Recognition

With the rise of decentralized computing, as in the Internet of Things, autonomous driving, and personalized healthcare, it is increasingly important to process time-dependent signals at the edge efficiently: right at the place where the temporal data are collected, avoiding time-consuming, insecure, and costly communication with a centralized computing facility (or cloud). However, modern-day processors often cannot meet the restrained power and time budgets of edge systems because of intrinsic limitations imposed by their architecture (von Neumann bottleneck) or domain conversions (analogue-to-digital and time-to-frequency). Here, we propose an edge temporal-signal processor based on two in-materia computing systems for both feature extraction and classification, reaching a software-level accuracy of 96.2% for the TI-46-Word speech-recognition task. First, a nonlinear, room-temperature dopant-network-processing-unit (DNPU) layer realizes analogue, time-domain feature extraction from the raw audio signals, similar to the human cochlea. Second, an analogue in-memory computing (AIMC) chip, consisting of memristive crossbar arrays, implements a compact neural network trained on the extracted features for classification. With the DNPU feature extraction consuming 100s nW and AIMC-based classification having the potential for less than 10 fJ per multiply-accumulate operation, our findings offer a promising avenue for advancing the compactness, efficiency, and performance of heterogeneous smart edge processors through in-materia computing hardware.

eess.AS↗

arXiv2025

Understanding the Growth and Properties of Sputter-Deposited Phase-Change Superlattice Films

Highly textured chalcogenide films have recently gained significant interest for phase-change memory applications. Several reports have highlighted that programming efficiency improves in devices featuring superlattice stacks, such as Ge2Sb2Te5/Sb2Te3. However, to be technologically relevant, these films must be deposited on foundry-scale wafers using processes compatible with back end of the line (BEOL) integration and complementary metal-oxide-semiconductor (CMOS) technology, such as, for example, sputter deposition. In this work, we present our observations on the influence of temperature, pressure, and seeding layer parameters on the sputter growth processes of superlattice films. By measuring various material properties, we construct a pseudo-phase diagram to illustrate the growth of both individual and superlattice films with different periodicities on technologically relevant substrates, namely SiO2 and carbon. These results provide important insights into the structure, intermixing and electro-optical properties of superlattice films,

cond-mat.mtrl-sci↗

arXiv2024

Roadmap to Neuromorphic Computing with Emerging Technologies

The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, addressing material challenges, exploring novel computing concepts, and finally examining the maturity level of emerging technologies while determining the next essential steps for their advancement.

eess.SP↗