A Soft-Error-Tolerant SAR ADC with Dual-Capacitor Sample-and-Hold Control for Sensor Systems.

For a reliable and stable sensor system, it is essential to precisely measure various sensor signals, such as electromagnetic field, pressure, and temperature. The measured analog signal is converted into digital bits through the sensor readout system. However, in extreme radiation environments, such as in space, during flights, and in nuclear fusion reactors, the performance of the analog-to-digital converter (ADC) constituting the sensor readout system can be degraded due to soft errors caused by radiation effects, leading to system malfunction. This paper proposes a soft-error-tolerant successive-approximation-register (SAR) ADC using dual-capacitor sample-and-hold (S/H) control, which has robust characteristics against total ionizing dose (TID) and single event effects (SEE). The proposed ADC was fabricated using 65-nm CMOS process, and its soft-error-tolerant performance was measured in radiation environments. Additionally, the proposed circuit techniques were verified by utilizing a radiation simulator CAD tool.

Retention-aware zero-shifting technique for Tiki-Taka algorithm-based analog deep learning accelerator.

We present the fabrication of 4 K-scale electrochemical random-access memory (ECRAM) cross-point arrays for analog neural network training accelerator and an electrical characteristic of an 8 × 8 ECRAM array with a 100% yield, showing excellent switching characteristics, low cycle-to-cycle, and device-to-device variations. Leveraging the advances of the ECRAM array, we showcase its efficacy in neural network training using the Tiki-Taka version 2 algorithm (TTv2) tailored for non-ideal analog memory devices. Through an experimental study using ECRAM devices, we investigate the influence of retention characteristics on the training performance of TTv2, revealing that the relative location of the retention convergence point critically determines the available weight range and, consequently, affects the training accuracy. We propose a retention-aware zero-shifting technique designed to optimize neural network training performance, particularly in scenarios involving cross-point devices with limited retention times. This technique ensures robust and efficient analog neural network training despite the practical constraints posed by analog cross-point devices.

Device-Algorithm Co-Optimization for an On-Chip Trainable Capacitor-Based Synaptic Device with IGZO TFT and Retention-Centric Tiki-Taka Algorithm.

Analog in-memory computing synaptic devices are widely studied for efficient implementation of deep learning. However, synaptic devices based on resistive memory have difficulties implementing on-chip training due to the lack of means to control the amount of resistance change and large device variations. To overcome these shortcomings, silicon complementary metal-oxide semiconductor (Si-CMOS) and capacitor-based charge storage synapses are proposed, but it is difficult to obtain sufficient retention time due to Si-CMOS leakage currents, resulting in a deterioration of training accuracy. Here, a novel 6T1C synaptic device using only n-type indium gaIlium zinc oxide thin film transistor (IGZO TFT) with low leakage current and a capacitor is proposed, allowing not only linear and symmetric weight update but also sufficient retention time and parallel on-chip training operations. In addition, an efficient and realistic training algorithm to compensate for any remaining device non-idealities such as drifting references and long-term retention loss is proposed, demonstrating the importance of device-algorithm co-optimization.