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Hanglin He

Publications and source records attributed to Hanglin He.

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Elemental Germanium Phase-Change Memory

Phase-change memory (PCM) is a mature technology for fast, scalable, non-volatile data storage, with applications spanning embedded memory, as well as in-memory and neuromorphic computing. PCM predominantly relies on chalcogenide alloys, with $\mathrm{Ge_2Sb_2Te_5}$ (GST) as the industry standard. Yet in these alloys, the individual Ge, Sb, and Te atoms redistribute upon cycling, causing stochastic operation and ultimately device failure. To address this issue, elemental antimony was proposed as a PCM material, but it exhibits a metastable amorphous state that prevents reliable data retention. Moreover, tellurium and antimony can contaminate complementary metal-oxide-semiconductor (CMOS) production lines or act as unintended dopants, restricting manufacturing of PCM to dedicated fabs. Here we introduce elemental germanium (Ge) as a CMOS-native phase-change material that overcomes these fundamental limitations. In a vertical PCM cell architecture, Ge enables sub-nanosecond crystallization (240 ps, 40 times faster than GST), non-volatile data storage with excellent thermal stability ($>$110 {\deg}C for 10 years vs. $\sim$87 {\deg}C for GST), and a resistance drift coefficient approximately 60% lower than in GST. These results establish pure Ge, a standard semiconductor, as an alternative to chalcogenide phase-change materials, achieving superior performance in key metrics and enabling phase-change memory to be fabricated in standard semiconductor facilities.

cond-mat.mtrl-sci

Conductance-dependent Photoresponse in a Dynamic SrTiO3 Memristor for Biorealistic Computing

Modern computers perform pre-defined operations using static memory components, whereas biological systems learn through inherently dynamic, time-dependent processes in synapses and neurons. The biological learning process also relies on global signals - neuromodulators - who influence many synapses at once depending on their dynamic, internal state. In this study, using optical radiation as a global neuromodulatory signal, we investigate nanoscale SrTiO3 (STO) memristors that can act as solid-state synapses. Via diverse sets of measurements, we demonstrate that the memristor's photoresponse depends on the electrical conductance state, following a well-defined square root relation. Additionally, we show that the conductance decays after photoexcitation with time constants in the range of 1 - 10 s and that this effect can be reliably controlled using an electrical bias. These properties in combination with our device's low power operation (< 1pJ per optical pulse) and small measurement variability may pave the way for space- and energy-efficient implementations of complex biological learning processes in electro-optical hardware.

cs.ET