Process influences on the microstructure of BEoL integrated ferroelectric hafnium zirconium oxide

Lederer, Maximilian; Lehninger, David; Abdulazhanov, Sukhrob; Reck, André; Olivo, Ricardo; Kämpfe, Thomas; Seidel, Konrad

doi:10.1109/isaf51943.2021.9477392

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…As shown in Figure a, since the randomness of domain complete switching is much smaller than that of partial switching, the C2C uniformity of the multiple polarization states in TiN/IGZO/AFE/TiN, especially the intermediate states 10 and 01, is much better than that of TiN/FE/TiN devices, which is mainly due to the switching of the intermediate states in TiN/IGZO/AFE/TiN device being complete. Furthermore, as shown in Figure b, the D2D uniformity of the multiple polarization states of TiN/IGZO/AFE/TiN is much better than that of TiN/FE/TiN devices, which may be due to the decrease of grain size of the HZO material with the increase of the Zr component . The decrease in grain size leads to an increase in the number of ferroelectric domains in the same area, which solves the problem of increased randomness caused by the reduction in the number of domains due to device area scaling.…”

Section: Resultsmentioning

confidence: 93%

“…Furthermore, as shown in Figure 6b, the D2D uniformity of the multiple polarization states of TiN/ IGZO/AFE/TiN is much better than that of TiN/FE/TiN devices, which may be due to the decrease of grain size of the HZO material with the increase of the Zr component. 19 The decrease in grain size leads to an increase in the number of ferroelectric domains in the same area, which solves the problem of increased randomness caused by the reduction in the number of domains due to device area scaling. On the other hand, the randomness of switching will be amplified when partial switching occurs, 11 so the variation of intermediate states 10 and 01 for the TiN/FE/TiN device is large.…”

Section: Multiple Steady-state Polarization Switchingmentioning

confidence: 99%

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Highly Reliable Logic-in-Memory by Bidirectional Built-in Electric-Field-Modulated Multistate IGZO/AFE Nonvolatile Memory

Tian

Chen

Yan

et al. 2023

ACS Appl. Electron. Mater.

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Highly reconfigurable logic-in-memory (LIM) cells based on emerging HfO 2doped ferroelectric (FE) memory with multiple polarization states have received extensive attention in overcoming the bottleneck of a von Neumann architecture with flexible and efficient computing ability. However, the variation of polarization states in device-to-device (D2D) and cycle-to-cycle (C2C) caused by partial polarization switching seriously plagues the large-scale integration of LIM based on multistate FE memory. Here, a highly reliable reconfigurable LIM with parallel computing capability is proposed based on an indium-gallium-zinc-oxide (IGZO)modulated multistate antiferroelectric (AFE) nonvolatile memory (NVM) with complete switching of different polarization states. The nonvolatile multiple polarization states of the AFE NVM are realized by introducing the bidirectional transformation of a built-in electric field in the memory, which is modulated by the accumulation or depletion of IGZO insertion layers between the electrodes and the AFE layer. The improved variation of C2C and D2D benefited from a nonoverlapping multiple coercive electric field (E c ) distribution and step-by-step polarization switching in AFE memory. Based on the proposed multistate AFE NVM, 2 logical operations are executed in parallel and 16 Boolean logic functions are constructed, including the 14 Boolean logic functions realized by a single device in 2 steps and the other 2 Boolean logic function realized by two devices in 4 steps. In addition, a 1-bit binary full subtractor and full adder can be realized efficiently using the multistate AFE NVM in parallel by fewer devices and operation steps, indicating that the AFE NVM is promising for developing larger scale efficient LIM with high reliability.

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Section: Resultsmentioning

confidence: 93%

Section: Multiple Steady-state Polarization Switchingmentioning

confidence: 99%

Highly Reliable Logic-in-Memory by Bidirectional Built-in Electric-Field-Modulated Multistate IGZO/AFE Nonvolatile Memory

Tian

Chen

Yan

et al. 2023

ACS Appl. Electron. Mater.

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“…Before wafers could be looped with X-FAB, the FE HZO films were optimized to meet the BEoL requirements [13], [14], [17], [18] and to stabilize a large fraction of the orthorhombic phase [19], [20], [21]. However, since these preoptimizations were done using a Fraunhofer IPMS internal platform, not all of these results can be transferred one-to-one to the loop wafers investigated here.…”

Section: Methodsmentioning

confidence: 99%

Ferroelectric FETs With Separated Capacitor in the Back-End-of-Line: Role of the Capacitance Ratio

Lehninger

Hoffmann

Sünbül

et al. 2022

IEEE Electron Device Lett.

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Hafnium oxide based ferroelectric (FE) memory concepts like FE field effect transistors (FeFETs) will become increasingly important. They are highly scalable, provide high operation speed, and consume low power. Just recently, an 1T1C FeFET concept with one transistor (1T) and one separated FE capacitor (1C) was demonstrated. This alternative approach is promising to overcome the drawbacks usually observed for the classic 1T concept like limited endurance, reduced retention, and high device-todevice variability. Electrically, the 1T1C FeFET consists of a series connection of the FE capacitor and the gate oxide capacitance. To operate at low voltages, a large fraction of the applied voltage must drop across the FE, which can be achieved by optimizing the capacitance ratio. Herein, 1T1C FeFETs with various capacitance ratios are fabricated and its impact on the electrical performance is discussed. Furthermore, the observed endurance of up to 10 8 field cycles illustrates the great potential of the new concept.

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“…This includes the grain structure, local crystallographic phase and local crystallographic orientation.The introduction of transmission Kikuchi diffraction as a viable method to study hafnium oxide enabled a detailed study of these parameters [62]. As expected from the discussion on thermodynamics in section , the number of monoclinic grains increases for low doping concentrations [63]. Moreover, the monoclinic phase fraction increases when the annealing temperatures are in the range or higher than 1000°C [64,65].…”

Section: Microstructure-based Variability In Ferroelectric Responsementioning

confidence: 95%

“…In addition to differences in the monoclinic phase fraction, grain size and crystallographic texture are influenced by changes in the process conditions. First of all, it has been shown that different dopants result in different grain sizes [63,66], with e.g. Si exhibiting much larger grains compared to HZO (Fig.…”

Section: Microstructure-based Variability In Ferroelectric Responsementioning

confidence: 99%

Monolithic-3D Inference Engine with IGZO Based Ferroelectric Thin Film Transistor Synapses

De¹,

Lederer²,

Raffel³

et al. 2023

Preprint

Self Cite

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<p> Instigated by the plethora of data generated by edge devices and IoT devices, machine learning has become the de facto choice of everyone for solving many tasks. Applications such as intelligent healthcare monitoring systems, smart watches, or automatic cars require real-time processing of the data or image, which is done by machine learning algorithms with higher efficiency than humans. There are two possible methods for artificial intelligence?1. non-von-Neumann hardware-based implementation of neural networks 2. traditional computer science based approach for neural networks or traditional von-Neumann architecture?based implementation of neural networks The standard von-Neumann performance of neural networks, where the memory and computation parts are segregated, suffers from severe latency with increasing number of edge devices. However, the multitude of edge devices used in our daily life imposes strict restrictions on latency, device area, and power consumption for hardware. Therefore, we need to take the route beyond the CMOS-based mixed-signal implementation of neural networks, where the memory bandwidth is not limited by the quintessential von-Neumann archi?tecture. The primary motivation of present-day research on the non-von-Neumann computing architecture is to build dedicated hardware modules for implementing low-power, fast-computing units without affecting the recent trend of scaling. This chapter focuses on amorphous indium-gallium-zinc-oxide (α-IGZO) based and their system-level applications. Back-end-of-line (BEoL) compatible indium IGZO based multibit one-time programmable (OTP) ferroelectric thin film transistors (FeTFT) with lifelong retention capability were fabricated. The maximum temperature of the entire fabrication process was limited to 350oC. The gate-stack engineering by varying the thickness ratio of ferroelectric hafnium zirconium oxide (HZO) and IGZO layer fomented excellent data-retention capability and one time programming property. Further, we have evaluated the performance of IGZO-based FeTFT as synaptic devices for an inference engine. The system-level simulation revealed inference accuracy loss of only 1.5% after ten years without re-training for Modified National Institute of Standards and Technology (MNIST) hand-written digits in a multi-layer perceptron (MLP) neural network with a baseline of 97%. The proposed inference engine also showed superior energy efficiency and cell area of 95.33 TOPS/W (binary) and 8F2 , respectively. </p>

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Process influences on the microstructure of BEoL integrated ferroelectric hafnium zirconium oxide

Cited by 7 publications

References 14 publications

Highly Reliable Logic-in-Memory by Bidirectional Built-in Electric-Field-Modulated Multistate IGZO/AFE Nonvolatile Memory

Highly Reliable Logic-in-Memory by Bidirectional Built-in Electric-Field-Modulated Multistate IGZO/AFE Nonvolatile Memory

Ferroelectric FETs With Separated Capacitor in the Back-End-of-Line: Role of the Capacitance Ratio

Monolithic-3D Inference Engine with IGZO Based Ferroelectric Thin Film Transistor Synapses

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