Inorganic Dual-Gate ZnO–Te Anti-Ambipolar Switches for Electrically Programmable Window Detection and Multi-Level Voltage Classification
The anti-ambipolar switch (AAS) produces a bell-shaped (Λ) transfer characteristic that embeds two threshold edges within a single device; however, this feature has been utilized mainly for discrete logic and periodic signals, rather than for discriminating a continuous input against defined voltage thresholds. Herein, a dual-gate ZnO–Te AAS (DGAAS), combined with a load transistor into an AAS cell, functions as an electrically programmable window detector. The bounded conduction interval of its Λ-curve defines the detection window, and the top-gate bias relocates this window without changes to the circuit topology. Fabricated at ≤150 °C, the device translates its peak by 0.95 V per volt of top-gate bias, with a device-to-device standard deviation of 29.4 mV across 30 devices. A six-transistor window detector, comprising the AAS cell and an output buffer, replaces the two-comparator-and-AND structure of a conventional CMOS design, reducing the transistor count by 75–77%, and circuit simulations based on the measured cell characteristics show that three cells map a continuous analog input to a 2-bit digital output. These results indicate that the ZnO–Te DGAAS serves as a compact primitive for voltage-domain signal discrimination, connecting analog sensing and digital readout within a back-end-of-line (BEOL)-compatible process.
Authors
- Jun-Hyeok Choi (ORCID: https://orcid.org/0000-0002-8696-2037)
- Shin Hyeon-Seok
- J. Jo (ORCID: https://orcid.org/0009-0005-6686-5124)
- Byoung Hun Lee (ORCID: https://orcid.org/0000-0002-4540-7731)
- Jae Hyeon Jun (ORCID: https://orcid.org/0009-0009-5253-1835)
- Chan Bin Lee
- Byeong Gwan Kim (ORCID: https://orcid.org/0009-0000-9042-6192)
- Jae Min Jo (ORCID: https://orcid.org/0009-0004-6712-3838)
- Jun Young Hong
Institutions
- Pohang University of Science and Technology (KR)
- Research Institute of Industrial Science and Technology (KR)
Publication Details
- Journal
- Nanomaterials
- Published
- 2026-11-04
- DOI
- https://doi.org/10.3390/nano16211418
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00