Scalable Ternary Logic Device Based on TeOx/IGTO Heterojunction Transistor

Abstract In today's era of rapid data proliferation, interconnect bottlenecks pose major challenges for conventional binary computing systems. These challenges encompass not only managing vast data volumes but also the substantial power consumption associated with data processing. To address these limitations, ternary logic systems have attracted considerable interest due to their capability to process information at higher densities within the same physical footprint compared to binary logic systems. Here, scalable ternary logic devices are demonstrated that are fully compatible with complementary‐metal‐oxide‐semiconductor (CMOS) technology. These devices are based on p‐n heterojunction thin‐film transistors (TFTs) composed of InGaSnO and TeO X , fabricated using a low processing temperature of 150 °C. The resulting heterojunction TFTs exhibit highly reproducible negative differential transconductance behavior with a peak‐to‐valley current ratio exceeding 10 3 . By monolithically integrating these high‐performance p‐n heterojunction TFTs with p‐channel TeO X TFTs, ternary logic devices are demonstrated. The optimized device architecture facilitates sophisticated transconductance matching, enabling the realization of a stable intermediate logic state. The use of low‐temperature, CMOS‐compatible processes ensures scalability and industrial compatibility, highlighting oxide semiconductors as a compelling material platform for next‐generation ternary logic technologies. This work is a pivotal advancement toward energy‐efficient, high‐density ternary systems, paving the way for transformative computational paradigms.

Authors

Institutions

Publication Details

Journal
Advanced Functional Materials
Published
2025-05-19
DOI
https://doi.org/10.1002/adfm.202501834
Citations
5
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
3.65

Funders

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

Scalable Ternary Logic Device Based on TeOx/IGTO Heterojunction Transistor

Jae Kyeong Jeong, C. R. Jung, Seong Hun Yoon, Taikyu Kim et al.
5 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
3.65
article

Scalable Ternary Logic Device Based on TeOx/IGTO Heterojunction Transistor

Jae Kyeong Jeong, C. R. Jung, Seong Hun Yoon, Taikyu Kim, Myung Mo Sung, Jun Ho Lee, Sung Heo
article en
5 citations

Abstract

Abstract In today's era of rapid data proliferation, interconnect bottlenecks pose major challenges for conventional binary computing systems. These challenges encompass not only managing vast data volumes but also the substantial power consumption associated with data processing. To address these limitations, ternary logic systems have attracted considerable interest due to their capability to process information at higher densities within the same physical footprint compared to binary logic systems. Here, scalable ternary logic devices are demonstrated that are fully compatible with complementary‐metal‐oxide‐semiconductor (CMOS) technology. These devices are based on p‐n heterojunction thin‐film transistors (TFTs) composed of InGaSnO and TeO X , fabricated using a low processing temperature of 150 °C. The resulting heterojunction TFTs exhibit highly reproducible negative differential transconductance behavior with a peak‐to‐valley current ratio exceeding 10 3 . By monolithically integrating these high‐performance p‐n heterojunction TFTs with p‐channel TeO X TFTs, ternary logic devices are demonstrated. The optimized device architecture facilitates sophisticated transconductance matching, enabling the realization of a stable intermediate logic state. The use of low‐temperature, CMOS‐compatible processes ensures scalability and industrial compatibility, highlighting oxide semiconductors as a compelling material platform for next‐generation ternary logic technologies. This work is a pivotal advancement toward energy‐efficient, high‐density ternary systems, paving the way for transformative computational paradigms.

Advanced Functional MaterialsVol. 35(44)
Hanyang University (KR), Stanford University (US)
National Research Foundation of Korea
Openalex Percentile: Top 7%
Advanced Memory and Neural Computing
3.65
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.