中枢神経系疾患の治療は正常細胞（ニューロン）の機能維持を目的とするが，脳血管障害のように機能障害の原因が細胞の死滅に基づくことは多い．一方，脳腫瘍の治療においては薬物療法や放射線療法といった腫瘍細胞の死滅を目標とするものが大きな位置を占める．いずれの場合にも，細胞死の機序を理解することは各種病態や治療法の理解のうえで重要である．現在のところ最も研究の進んでいる細胞死の型はアポトーシスである．そのなかで重要な位置を占めるミトコンドリアにおける反応および抗アポトーシス因子について概要を紹介する．

Neuroscience 細胞死：最近の知見

The existence of reversible resistance switching (RS) behaviors induced by electric stimulus has been known for some time, and these intriguing physical phenomena have been observed in numerous materials, including oxides. As conventional charge-based random access memory is expected to face a size limit in the near future, a surge of renewed interest has been developed in RS phenomena for possible applications in small nonvolatile memory devices called resistance random access memory (RRAM). Of particular interest is unipolar RS, which shows the RS at two values of applied voltage of the same polarity. The unipolar RS exhibits a much larger resistance change than other RS phenomena, and this greatly simplifies the process of reading the memory state. When fabricated with oxide p-n diodes, memory cells using unipolar RS can be stacked vertically, which has the potential for dramatically increasing memory density. Therefore, unipolar RRAM may be a good candidate for multi-stacked, high density, nonvolatile memory. The most important scientific and technical issues concerning unipolar RS are how it works and the identification of its controlling parameters. Some studies have reported that unipolar RS comes from a homogeneous/inhomogeneous transition of current distribution, while others maintain that it comes from the formation and rupture of conducting filaments. Even with recent extensive studies on unipolar RS, its basic origin is still far from being understood. In addition, no model exists that actually explains how the reversible switching can occur at two values of applied voltage. This lack of a quantitative model poses a major barrier for unipolar RRAM applications. In this study, we describe RS behavior in polycrystalline TiO2 film. To explain the basic mechanism of unipolar RS behavior, we propose a new percolation model based on a network of ‘‘circuit breakers’’ with two switchable metastable states. The random circuit breaker (RCB) network model can explain the long-standing material issue of how unipolar RS occurs. This simple percolation model is different from the conventional percolation models, which have dealt only with static or irreversible dynamic processes. In addition, the RCB network model provides an indication of how to overcome the substantial distribution of switching voltages, which is currently considered the most serious obstacle to practical unipolar RRAM applications. The unipolar RS phenomenon can be explained by the current (I)-voltage (V) curves in Figure 1a, which are derived from measurements of our polycrystalline TiO2 thin capacitors. At the pristine state (green dot), they are in an insulating state. As the external voltage Vext increases from zero and reaches a threshold voltage Vforming, a sudden increase occurs in the current. If the current is not limited to a certain value, here called the compliance current Icomp, the TiO2 capacitor would experience a dielectric breakdown and be destroyed. However, [*] Prof. T. W. Noh, S. C. Chae, S. B. Lee, S. H Chang, Dr. C. Liu ReCOE & FPRD, Department of Physics and Astronomy Seoul National University Seoul 151-747 (Korea) E-mail: twnoh@snu.ac.kr

Random circuit breaker network model for unipolar resistance switching (Advanced Materials (2008) 20 (1154))

Memristive Synapses and Neurons for Bioinspired Computing

In this article, we review the existing analog resistive switching memory (RSM) devices and their hardware technologies for in-memory learning, as well as their challenges and prospects. Since the characteristics of the devices are different for in-memory learning and digital memory applications, it is important to have an in-depth understanding across different layers from devices and circuits to architectures and algorithms. First, based on a top-down view from architecture to devices for analog computing, we define the main figures of merit (FoMs) and perform a comprehensive analysis of analog RSM hardware including the basic device characteristics, hardware algorithms, and the corresponding mapping methods for device arrays, as well as the architecture and circuit design considerations for neural networks. Second, we classify the FoMs of analog RSM devices into two levels. Level 1 FoMs are essential for achieving the functionality of a system (e.g., linearity, symmetry, dynamic range, level numbers, fluctuation, variability, and yield). Level 2 FoMs are those that make a functional system more efficient and reliable (e.g., area, operational voltage, energy consumption, speed, endurance, retention, and compatibility with back-end-of-line processing). By constructing a device-to-application simulation framework, we perform an in-depth analysis of how these FoMs influence in-memory learning and give a target list of the device requirements. Lastly, we evaluate the main FoMs of most existing devices with analog characteristics and review optimization methods from programming schemes to materials and device structures. The key challenges and prospects from the device to system level for analog RSM devices are discussed.

In-memory Learning with Analog Resistive Switching Memory: A Review and Perspective

Emerging flexible artificial sensory systems using neuromorphic electronics have been considered as a promising solution for processing massive data with low power consumption. The construction of artificial sensory systems with synaptic devices and sensing elements to mimic complicated sensing and processing in biological systems is a prerequisite for the realization. To realize high-efficiency neuromorphic sensory systems, the development of artificial flexible synapses with low power consumption and high-density integration is essential. Furthermore, the realization of efficient coupling between the sensing element and the synaptic device is crucial. This Review presents recent progress in the area of neuromorphic electronics for flexible artificial sensory systems. We focus on both the recent advances of artificial synapses, including device structures, mechanisms, and functions, and the design of intelligent, flexible perception systems based on synaptic devices. Additionally, key challenges and opportunities related to flexible artificial perception systems are examined, and potential solutions and suggestions are provided.

Flexible Artificial Sensory Systems Based on Neuromorphic Devices.

The electronic synapse, which can vividly emulate short-term and long-term plasticity, as well as voltage sensitivity, in the bio-synapse, is the vital device foundation for brain-inspired neuromorphic computing. In this letter, we propose a Ag/GeSe/TiN memristor as an electronic synapse for brain-inspired neuromorphic applications. Due to the electromigration and diffusion of Ag cation, the volatile and non-volatile switching behaviours are coexistent in this device. Various synaptic functions, including short-term plasticity, long-term plasticity, pair-pulse facilitation, and spike timing-dependent plasticity, have been successfully eliminated in Ag/GeSe/TiN devices. Furthermore, all the synaptic functions are induced by the spiking stimuli with amplitudes of several hundred millivolts. All the results demonstrate that the Ag/GeSe/TiN device has great potential for brain-inspired computing systems in the future.

Short-Term and Long-Term Plasticity Mimicked in Low-Voltage Ag/GeSe/TiN Electronic Synapse

Electronic synapse with precise analog weight tuning ability and long-term retention is the vital device foundation of memristor-based neuromorphic computing systems In this letter, we propose a Ti/AlO x /TaO x /Pt memristor as an analog synapse for memristive neural network applications The device shows high uniformity, excellent analog switching behaviors (up to 200 resistance states under triangle pulses) and excellent long-term retention of each state (up to 30 000 s) Furthermore, the precise modulation of the device resistance state (with 17% tolerance) can also be achieved by a finer writing program within 50 cycles

A Ti/AlO x /TaO x /Pt Analog Synapse for Memristive Neural Network

Programmable metallization cell is one of important threshold switching selectors. We first performed a study on the selector based on amorphous chalcogenide material (Si0.4Te0.6) because of the rigid structure applied in ovonic threshold switch. In the meantime, annealing process is implemented to improve the performance. Results show that devices without annealing process demonstrate a minor threshold switching characteristic, revealing the potential as selector for cross-point memristor array. After implementing annealing process, threshold voltage ( $V _{\mathrm{ th}}$ ), selectivity and endurance of selectors improve. Meanwhile, requirements of high current and a low holding voltage ( $V _{\mathrm{ h}}$ ) for an ideal selector are fulfilled. Using the Ag filament formed during motion of Ag ions, a steep-slope (1.7 mV/dec) for threshold switching with high selectivity (~104) could be achieved. Owing to the faster diffusivity of Ag atoms in solid-electrolytes, the resulting Ag filament easily dissolved under low current regime. It is deduced that performance improvement is due to the defect reduction within annealing process. Finally, time characteristics of selector devices are tested to verify fast switching and recovery speed for practical applicability.

Threshold Switching Behavior of Ag-SiTe-Based Selector Device and Annealing Effect on its Characteristics

Volatile programmable metallization cell is a promising threshold switching selector with excellent characteristics and simple structures. However, the large variation of threshold voltage is a major problem for practical application. In this work, we propose a dual-layer structure to increase selectivity and improve the threshold voltage variation. Compared to single-layer devices, this dual-layer device exhibits higher selectivity (>107) and better threshold voltage uniformity with less than 5% fluctuation during 200 DC switching. The improvement is attributed to good control on the location of the filament formation and rupture after introducing a HfO2 layer. It is deduced that a major factor consists of the difference of Ag ions mobility between SiTe and HfO2 due to the grain boundary quantity.

https://www.mdpi.com/2079-4991/9/3/408/pdf

A HfO₂/SiTe Based Dual-Layer Selector Device with Minor Threshold Voltage Variation.

In this work, an ovonic threshold switching (OTS) selector with simply binary GeSe has been demonstrated. According to mean coordination number theory, Se-rich GeSe was chosen as the dielectric layer. Meanwhile, annealing process was attempted to reduce defect quantity and enhance atomic structure stability. The results showed improving selector performance such as low off current (  1 mA at ~ 2 V), extremely sharp switching slope (< 3 mv/dec), fast operating speed (turn-on time < 100 ns). In addition, the mechanism of trap-assisted Poole–Frenkel conduction has been deduced to explain the reasons for improvements induced by annealing.

Haijun Liu

Papers

Short-Term and Long-Term Plasticity Mimicked in Low-Voltage Ag/GeSe/TiN Electronic Synapse

A Ti/AlO x /TaO x /Pt Analog Synapse for Memristive Neural Network

Threshold Switching Behavior of Ag-SiTe-Based Selector Device and Annealing Effect on its Characteristics

A HfO₂/SiTe Based Dual-Layer Selector Device with Minor Threshold Voltage Variation.

An ovonic threshold switching selector based on Se-rich GeSe chalcogenide