Accumulative Polarization Reversal in Nanoscale Ferroelectric Transistors

The electric-field-driven and reversible polarization switching in ferroelectric materials provides a promising approach for nonvolatile information storage. With the advent of ferroelectricity in hafnium oxide, it has become possible to fabricate ultrathin ferroelectric films suitable for nanoscale electronic devices. Among them, ferroelectric field-effect transistors (FeFETs) emerge as attractive memory elements. While the binary switching between the two logic states, accomplished through a single voltage pulse, is mainly being investigated in FeFETs, additional and unusual switching mechanisms remain largely unexplored. In this work, we report the natural property of ferroelectric hafnium oxide, embedded within a nanoscale FeFET, to accumulate electrical excitation, followed by a sudden and complete switching. The accumulation is attributed to the progressive polarization reversal through localized ferroelectric nucleation. The electrical experiments reveal a strong field and time dependence of the phenomenon. These results not only offer novel insights that could prove critical for memory applications but also might inspire to exploit FeFETs for unconventional computing

Accelerate & Actualize: Can 2D Materials Bridge the Gap Between Neuromorphic Hardware and the Human Brain?

Two-dimensional (2D) materials present an exciting opportunity for devices and systems beyond the von Neumann computing architecture paradigm due to their diversity of electronic structure, physical properties, and atomically-thin, van der Waals structures that enable ease of integration with conventional electronic materials and silicon-based hardware. All major classes of non-volatile memory (NVM) devices have been demonstrated using 2D materials, including their operation as synaptic devices for applications in neuromorphic computing hardware. Their atomically-thin structure, superior physical properties, i.e., mechanical strength, electrical and thermal conductivity, as well as gate-tunable electronic properties provide performance advantages and novel functionality in NVM devices and systems. However, device performance and variability as compared to incumbent materials and technology remain major concerns for real applications. Ultimately, the progress of 2D materials as a novel class of electronic materials and specifically their application in the area of neuromorphic electronics will depend on their scalable synthesis in thin-film form with desired crystal quality, defect density, and phase purity.Comment: Neuromorphic Computing, 2D Materials, Heterostructures, Emerging Memory Devices, Resistive, Phase-Change, Ferroelectric, Ferromagnetic, Crossbar Array, Machine Learning, Deep Learning, Spiking Neural Network

Content Addressable Memories and Transformable Logic Circuits Based on Ferroelectric Reconfigurable Transistors for In-Memory Computing

As a promising alternative to the Von Neumann architecture, in-memory computing holds the promise of delivering high computing capacity while consuming low power. Content addressable memory (CAM) can implement pattern matching and distance measurement in memory with massive parallelism, making them highly desirable for data-intensive applications. In this paper, we propose and demonstrate a novel 1-transistor-per-bit CAM based on the ferroelectric reconfigurable transistor. By exploiting the switchable polarity of the ferroelectric reconfigurable transistor, XOR/XNOR-like matching operation in CAM can be realized in a single transistor. By eliminating the need for the complementary circuit, these non-volatile CAMs based on reconfigurable transistors can offer a significant improvement in area and energy efficiency compared to conventional CAMs. NAND- and NOR-arrays of CAMs are also demonstrated, which enable multi-bit matching in a single reading operation. In addition, the NOR array of CAM cells effectively measures the Hamming distance between the input query and stored entries. Furthermore, utilizing the switchable polarity of these ferroelectric Schottky barrier transistors, we demonstrate reconfigurable logic gates with NAND/NOR dual functions, whose input-output mapping can be transformed in real-time without changing the layout. These reconfigurable circuits will serve as important building blocks for high-density data-stream processors and reconfigurable Application-Specific Integrated Circuits (r-ASICs). The CAMs and transformable logic gates based on ferroelectric reconfigurable transistors will have broad applications in data-intensive applications from image processing to machine learning and artificial intelligence

In-memory computing with emerging memory devices: Status and outlook

Supporting data for "In-memory computing with emerging memory devices: status and outlook", submitted to APL Machine Learning

High-Performance and Energy-Efficient Leaky Integrate-and-Fire Neuron and Spike Timing-Dependent Plasticity Circuits in 7nm FinFET Technology

In designing neuromorphic circuits and systems, developing compact and energy-efficient neuron and synapse circuits is essential for high-performance on-chip neural architectures. Toward that end, this work utilizes the advanced low-power and compact 7nm FinFET technology to design leaky integrate-and-fire (LIF) neuron and spike-timing-dependent plasticity (STDP) circuits. In the proposed STDP circuit, only six FinFETs and three small capacitors (two 10fF and 20fF) have been utilized to realize STDP learning. Moreover, 12 transistors and two capacitors (20fF) have been employed for designing the LIF neuron circuit. The evaluation results demonstrate that besides 60% area saving, the proposed STDP circuit achieves 68% improvement in total average power consumption and 43% lower energy dissipation compared to previous works. The proposed LIF neuron circuit demonstrates 34% area saving, 46% power, and 40% energy saving compared to its counterparts. The neuron can also tune the firing frequency within 5MHz-330MHz using an external control voltage. These results emphasize the potential of the proposed neuron and STDP learning circuits for compact and energy-efficient neuromorphic computing systems