Results 81 to 90 of about 541 (202)

Materials Design Principles for Large Memory Windows: Coercive Voltage Engineering in Ferroelectric– Dielectric Heterostructures

Advanced Electronic Materials, EarlyView.
Coercive voltage enhancement in hafnia‐based ferroelectric–dielectric heterostructures is shown to originate from leakage‐governed voltage division between the ferroelectric and dielectric layers. Through experiments, circuit modeling, and defect‐based simulations, a universal framework is established to engineer large memory windows without altering ...
Prasanna Venkatesan, Hari Jayasankar, Salma Soliman, Priyankka Ravikumar, Lance Fernandes, Chinsung Park, Amrit Garlapati, Chengyang Zhang, Sanghyun Kang, Shimeng Yu, Suman Datta, Asif Khan, Mengkun Tian, Zheng Wang, Kijoon Kim, Kwangyou Seo, Kwangsoo Kim, Wanki Kim, Daewon Ha, Luca Larcher, Gaurav Thareja, Andrea Padovani   +21 more
wiley   +1 more source

Electrode‐Engineered Dual‐Mode Multifunctional Lead‐Free Perovskite Optoelectronic Memristors for Neuromorphic Computing

Advanced Electronic Materials, EarlyView.
A lead‐free perovskite memristive solar cell structure that call emulate both synaptic and neuronal functions controlled by light and electric fields depending on top electrode type. ABSTRACT Memristive devices based on halide perovskites hold strong promise to provide energy‐efficient systems for the Internet of Things (IoT); however, lead (Pb ...
Michalis Loizos, Konstantinos Rogdakis, Konstantinos Chatzimanolis, Katerina Anagnostou, Emmanuel Kymakis   +4 more
wiley   +1 more source

High‐Density and Scalable Graphene Hall Sensor Arrays Through Monolithic CMOS Integration

Advanced Electronic Materials, EarlyView.
This work explores strategies to create high‐density arrays of graphene Hall‐effect magnetic field sensors through monolithic integration with silicon CMOS biasing and multiplexing circuitry. High sensor yield and magnetic sensing performance are achieved through careful choices in the custom CMOS chip design and the graphene integration process.
Vasant Iyer, Nishal Shah, A. T. Charlie Johnson, David A. Issadore, Firooz Aflatouni   +4 more
wiley   +1 more source

Model‐Based Time‐Modulated Write Algorithm for 1R Analog Memristive Crossbar Arrays

Advanced Electronic Materials, EarlyView.
A novel model‐based time‐modulated write algorithm efficiently programs analog 1R memristive crossbar arrays by varying pulse duration at a fixed voltage. By leveraging a physics‐based compact model and a dynamic gain mechanism, this approach overcomes device nonlinearities and parasitic effects.
Richard Schroedter, Ahmet Samil Demirkol, Ioannis Messaris, Christian Bruchatz, Eter Mgeladze, Stefan Slesazeck, Thomas Mikolajick, Ronald Tetzlaff   +7 more
wiley   +1 more source

A large-scale coherent 4D imaging sensor. [PDF]

Nature
Settembrini FF, Gungor AC, Forrer A, Fortune SA, Dell'Aquila A, Padmanabhan P, Opris IE, Sotto M, Dordevic N, Perelman Y, Christen T, Wang M, Nicolaescu R.   +12 more
europepmc   +1 more source

A Compendium of Logic Gates Based on Reconfigurable Three‐Independent‐Gate Transistors Realized in FDSOI Hardware

Advanced Electronic Materials, EarlyView.
This work electrically characterizes sixteen logic gates built from three‐independent‐gate reconfigurable transistors fabricated on full‐scale 300 mm wafers using the industrial 22 nm fully depleted FDSOI process of GlobalFoundries. Static and time‐resolved measurements confirm correct operation, including a 1‐bit adder and reconfigurable AOI/OAI ...
Juan P. Martinez, Yuxuan He, Giulio Galderisi, Violetta Sessi, Niladri Bhattacharjee, Peter Baars, Kerstin Poenisch, Annekathrin Zeun, Konstantin Li, Fernando Koch, Binit Syamal, Thomas Mikolajick, Jens Trommer   +12 more
wiley   +1 more source

A full-featured 2D flash chip enabled by system integration. [PDF]

Nature
Liu C, Jiang Y, Shen B, Yuan S, Cao Z, Bi Z, Wang C, Xiang Y, Wang T, Wu H, Liu Z, Wang Y, Wang S, Zhou P.   +13 more
europepmc   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

A ferroelectric-memristor memory for both training and inference. [PDF]

Nat Electron
Martemucci M, Rummens F, Malot Y, Hirtzlin T, Guille O, Martin S, Carabasse C, Vincent AF, Saïghi S, Grenouillet L, Querlioz D, Vianello E.   +11 more
europepmc   +1 more source

Ultra‐Low Power Consumption and Highly Durability in Sm:HfO2 Thin Film Ferroelectric Memristor for Edge Detection

Advanced Electronic Materials, EarlyView.
ABSTRACT With the continuous development of computer image processing, developing efficient and low‐power computing devices has become a key challenge. Memristors have integrated in‐situ storage and computing capabilities, making them an ideal choice for low‐power image processing computing architectures. However, current memristors are confronted with
Tengyu Li, YongRui Wang, Weifeng Zhang, Jikang Xu, Xiaobing Yan   +4 more
wiley   +1 more source