A Compact One-Transistor-Multiple-RRAM Characterization Platform

Emerging non-volatile memories (eNVMs) such as resistive random-access memory (RRAM) offer an alternative solution compared to standard CMOS technologies for implementation of in-memory computing (IMC) units used in artificial neural network (ANN) applications. Existing measurement equipment for device characterisation and programming of such eNVMs are usually bulky and expensive. In this work, we present a compact size characterization platform for RRAM devices, including a custom programming unit IC that occupies less than 1 mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of silicon area. Our platform is capable of testing one-transistor-one-RRAM (1T1R) as well as one-transistor-multiple-RRAM (1TNR) cells. Thus, to the best knowledge of the authors, this is the first demonstration of an integrated programming interface for 1TNR cells. The 1T2R IMC cells were fabricated in the IHP’s 130 nm BiCMOS technology and, in combination with other parts of the platform, are able to provide more synaptic weight resolution for ANN model applications while simultaneously decreasing the energy consumption by 50 %. The platform can generate programming voltage pulses with a 3.3 mV accuracy. Using the incremental step pulse with verify algorithm (ISPVA) we achieve 5 non-overlapping resistive states per 1T1R device. Based on those 1T1R base states we measure 15 resulting state combinations in the 1T2R cells.

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Publication Details

Journal
IEEE Transactions on Circuits and Systems I Regular Papers
Published
2025-04-18
DOI
https://doi.org/10.1109/tcsi.2025.3555234
Citations
2
Primary Topic
Semiconductor materials and devices
Type
article
Field-Weighted Citation Impact
1.46

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article

A Compact One-Transistor-Multiple-RRAM Characterization Platform

Andrea Baroni, Eduardo Pérez, P. Ostrovskyy, Corrado Carta et al.
2 citations
IEEE Transactions on Circuits and Systems I Regular Papers
Semiconductor materials and devices
1.46
article

A Compact One-Transistor-Multiple-RRAM Characterization Platform

Andrea Baroni, Eduardo Pérez, P. Ostrovskyy, Corrado Carta, Max Uhlmann, Miłosz Krysik, Keerthi Dorai Swamy Reddy, Jianan Wen, Christian Wenger, Krzysztof Piotrowski, Gerhard Kahmen, Max Frohberg
article en
2 citations

Abstract

Emerging non-volatile memories (eNVMs) such as resistive random-access memory (RRAM) offer an alternative solution compared to standard CMOS technologies for implementation of in-memory computing (IMC) units used in artificial neural network (ANN) applications. Existing measurement equipment for device characterisation and programming of such eNVMs are usually bulky and expensive. In this work, we present a compact size characterization platform for RRAM devices, including a custom programming unit IC that occupies less than 1 mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of silicon area. Our platform is capable of testing one-transistor-one-RRAM (1T1R) as well as one-transistor-multiple-RRAM (1TNR) cells. Thus, to the best knowledge of the authors, this is the first demonstration of an integrated programming interface for 1TNR cells. The 1T2R IMC cells were fabricated in the IHP’s 130 nm BiCMOS technology and, in combination with other parts of the platform, are able to provide more synaptic weight resolution for ANN model applications while simultaneously decreasing the energy consumption by 50 %. The platform can generate programming voltage pulses with a 3.3 mV accuracy. Using the incremental step pulse with verify algorithm (ISPVA) we achieve 5 non-overlapping resistive states per 1T1R device. Based on those 1T1R base states we measure 15 resulting state combinations in the 1T2R cells.

IEEE Transactions on Circuits and Systems I Regular PapersVol. 72(10)
Fraunhofer Research Institution for Energy Infrastructures and Geotechnologies IEG (DE), Leibniz Institute for High Performance Microelectronics (DE)
Deutsche Forschungsgemeinschaft
Affordable and clean energy
Openalex Percentile: Top 18%
Semiconductor materials and devices
1.46
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