arXiv subjects

Vara Prasad Jonnalagadda

Publications and source records attributed to Vara Prasad Jonnalagadda.

5 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↗

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↗

arXiv2021

Phase Change Memtransistive Synapse

In the mammalian nervous system, various synaptic plasticity rules act, either individually or synergistically, and over wide-ranging timescales to dictate the processes that enable learning and memory formation. To mimic biological cognition for artificial intelligence, neuromorphic computing platforms thus call for synthetic synapses, that can faithfully express such complex plasticity and dynamics. Although some plasticity rules have been emulated with elaborate CMOS and memristive circuitry, hardware demonstrations that combine multiple plasticities, such as long-term (LTP) and short-term plasticity (STP) with tunable dynamics and within the same low-power nanoscale devices have been missing. Here, we introduce phase change memtransistive synapse that leverages the non-volatility of memristors and the volatility of transistors for coupling LTP with homo and heterosynaptic STP effects. We show that such biomimetic synapses can enable some powerful cognitive frameworks, such as the short-term spike-timing-dependent plasticity (ST-STDP) and stochastic Hopfield neural networks. We demonstrate how, much like the mammalian brain, such emulations can establish temporal relationships in data streams for the task of sequential learning and combinatorial optimization.

cond-mat.dis-nn↗

arXiv2019

Monatomic phase change memory

Phase change memory has been developed into a mature technology capable of storing information in a fast and non-volatile way, with potential for neuromorphic computing applications. However, its future impact in electronics depends crucially on how the materials at the core of this technology adapt to the requirements arising from continued scaling towards higher device densities. A common strategy to finetune the properties of phase change memory materials, reaching reasonable thermal stability in optical data storage, relies on mixing precise amounts of different dopants, resulting often in quaternary or even more complicated compounds. Here we show how the simplest material imaginable, a single element (in this case, antimony), can become a valid alternative when confined in extremely small volumes. This compositional simplification eliminates problems related to unwanted deviations from the optimized stoichiometry in the switching volume, which become increasingly pressing when devices are aggressively miniaturized. Removing compositional optimization issues may allow one to capitalize on nanosize effects in information storage.

physics.app-ph↗