A Reconfigurable Ferroelectric Transistor as An Ultra-Scaled Cell for Low-Power In-Memory Data Processing
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| Titel: | A Reconfigurable Ferroelectric Transistor as An Ultra-Scaled Cell for Low-Power In-Memory Data Processing |
|---|---|
| Autoren: | Zhu, Zhongyunshen, Persson, Anton E.O., Wernersson, Lars Erik |
| Weitere Verfasser: | Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Electrical and Information Technology, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för elektro- och informationsteknik, Originator |
| Quelle: | Advanced Electronic Materials. 11(3) |
| Schlagwörter: | Natural Sciences, Computer and Information Sciences, Computer Engineering, Naturvetenskap, Data- och informationsvetenskap (Datateknik), Datorteknik |
| Beschreibung: | Compact in-memory computing architectures are desirable to embed artificial intelligence (AI) in resource-restricted edge devices. However, current technologies face limitations in both the area and energy efficiency. Here, a reconfigurable ferroelectric tunnel field-effect transistor (ferro-TFET) is presented that can be used as an ultra-scaled cell for low-power in-memory data processing. A gate-all-around ferroelectric film is integrated on a vertical nanowire TFET with a gate/source overlapped channel, enabling non-volatilely reconfigurable anti-ambipolarity by programming the ferroelectric polarization state. By considering the stored polarization state and reading voltage as inputs, an XNOR operation is achieved in a single-gate ferro-TFET. It is shown that the ferro-TFETs can be implemented in a crossbar array for convolutional frequency filtering whose performance can be evaluated by an impulse-response method considering the effect of device-to-device variation based on statistics. Benefiting from the miniaturized footprint, non-volatility, and low-power operation, ferro-TFETs show promises as a one-transistor in-memory computing cell for area- and energy-efficient edge AI applications. |
| Zugangs-URL: | https://doi.org/10.1002/aelm.202400335 |
| Datenbank: | SwePub |
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