Boosting Stochasticity in Ovonic Threshold Switches Through Cryogenic First Firing for Fast and Reliable Entropy Generation

Abstract As encryption demands at the edges grow, volatile switching devices have emerged as promising candidates for entropy sources because of their inherent stochastic properties, offering fast, energy‐efficient operation and a minimal footprint. Although most studies have focused on exploiting inherent stochasticity, efforts to analyze and optimize these devices to enhance their randomness remain scarce. In this study, the stochastic switching characteristics of Ovonic Threshold Switch (OTS) devices by controlling the first firing temperatures to amplify their inherent stochasticity are explored. It is demonstrated that firing at cryogenic temperatures (77 K) induces field‐dominant firing and a considerable increase in traps within the device. These additional traps lead to a substantial enhancement in switching variability, with the switching time fluctuation increasing up to four times compared to the first firing temperature of 298 K. Furthermore, a reference‐free entropy‐harvesting method is proposed that ensures robust and stable operation even under cycling degradation. Based on this approach, the OTS devices that undergo first firing at cryogenic temperatures achieve stable entropy generation at speeds exceeding 20 Mbit s −1 . This study demonstrates the potential of optimizing OTS devices to satisfy the increasing demand for fast and energy‐efficient entropy sources in advanced cryptographic systems.

Authors

Institutions

Publication Details

Journal
Advanced Electronic Materials
Published
2025-05-01
DOI
https://doi.org/10.1002/aelm.202400881
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Boosting Stochasticity in Ovonic Threshold Switches Through Cryogenic First Firing for Fast and Reliable Entropy Generation

Dongmin Kim, Jangseop Lee, Yoori Seo, Hyunsang Hwang et al.
1 citations
Advanced Electronic Materials
Advanced Memory and Neural Computing
0.73
article

Boosting Stochasticity in Ovonic Threshold Switches Through Cryogenic First Firing for Fast and Reliable Entropy Generation

Dongmin Kim, Jangseop Lee, Yoori Seo, Hyunsang Hwang, Ohhyuk Kwon, C. D. Kim, Jisung Lee, Joonhyun Kwon, Pendar Azaripour Masouleh
article en
1 citations

Abstract

Abstract As encryption demands at the edges grow, volatile switching devices have emerged as promising candidates for entropy sources because of their inherent stochastic properties, offering fast, energy‐efficient operation and a minimal footprint. Although most studies have focused on exploiting inherent stochasticity, efforts to analyze and optimize these devices to enhance their randomness remain scarce. In this study, the stochastic switching characteristics of Ovonic Threshold Switch (OTS) devices by controlling the first firing temperatures to amplify their inherent stochasticity are explored. It is demonstrated that firing at cryogenic temperatures (77 K) induces field‐dominant firing and a considerable increase in traps within the device. These additional traps lead to a substantial enhancement in switching variability, with the switching time fluctuation increasing up to four times compared to the first firing temperature of 298 K. Furthermore, a reference‐free entropy‐harvesting method is proposed that ensures robust and stable operation even under cycling degradation. Based on this approach, the OTS devices that undergo first firing at cryogenic temperatures achieve stable entropy generation at speeds exceeding 20 Mbit s −1 . This study demonstrates the potential of optimizing OTS devices to satisfy the increasing demand for fast and energy‐efficient entropy sources in advanced cryptographic systems.

Advanced Electronic MaterialsVol. 11(9)
Pohang University of Science and Technology (KR), Hyundai Motors (South Korea) (KR)
Hyundai Motor Group
Openalex Percentile: Top 29%
Advanced Memory and Neural Computing
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.