80Kb 10ns read cycle logic Embedded High-K charge trap Multi-Time-Programmable Memory scalable to 14nm FIN with no added process complexity

Charge Trap Transistor (CTT): An Embedded Fully Logic-Compatible Multiple-Time Programmable Non-Volatile Memory Element for High- $k$ -Metal-Gate CMOS Technologies

Abstract: The availability of on-chip non-volatile memory for advanced high- $k$ -metal-gate CMOS technology nodes has been limited due to integration and scaling challenges as well as operational voltage incompatibilities, while its need continues to grow rapidly in modern high-performance systems. By exploiting intrinsic device self-heating enhanced charge trapping in as fabricated high- $k$ -metal-gate logic devices, we introduce a unique multiple-time programmable embedded non-volatile memory element, called the ‘charge trap transistor’ (CTT), for high- $k$ -metal-gate CMOS technologies. Functionality and feasibility of using CTT memory devices have been demonstrated on 22 nm planar and 14 nm FinFET technology platforms, including fully functional product prototype memory arrays. These transistor memory devices offer high density ( $\sim 0.144\mu\mathrm{m}^{2}$ /bit for 22 nm and $\sim 0.082\mu\mathrm{m}^{2}$ /bit for 14 nm technology), logic voltage compatible and low peak power operation (~4mW), and excellent retention for a fully integrated and scalable embedded non-volatile memory without added process complexity or masks.

An Analog Neural Network Computing Engine Using CMOS-Compatible Charge-Trap-Transistor (CTT)

Abstract: An analog neural network computing engine based on CMOS-compatible charge-trap transistor (CTT) is proposed in this paper. CTT devices are used as analog multipliers. Compared to digital multipliers, CTT-based analog multiplier shows significant area and power reduction. The proposed computing engine is composed of a scalable CTT multiplier array and energy efficient analog–digital interfaces. By implementing the sequential analog fabric, the engine’s mixed-signal interfaces are simplified and hardware overhead remains constant regardless of the size of the array. A proof-of-concept 784 by 784 CTT computing engine is implemented using TSMC 28-nm CMOS technology and occupies 0.68 mm2. The simulated performance achieves 76.8 TOPS (8-bit) with 500 MHz clock frequency and consumes 14.8 mW. As an example, we utilize this computing engine to address a classic pattern recognition problem—classifying handwritten digits on MNIST database and obtained a performance comparable to state-of-the-art fully connected neural networks using 8-bit fixed-point resolution.

Unsupervised Learning Using Charge-Trap Transistors

Abstract: Unsupervised learning is demonstrated using a device ubiquitously found in today’s technology: a transistor with high- ${k}$ -metal gate. Specifically, the charge-trapping phenomenon in the high- ${k}$ gate dielectric is leveraged so that the device can be used as a non-volatile analog memory. Experimental data from 22-nm silicon-on-insulator devices reveal that a charge-trap transistor possesses promising characteristics for implementing synapses in neural networks, such as very fine tunability, weight-dependent plasticity, and low power consumption. A proof-of-concept winner-takes-all neural network is simulated based on experimental data and perfect clustering is achieved within tens of training cycles. This means that the network can be trained for multiple times, and a larger system can be built. The robustness of the procedure to the device variation is also discussed.

80-kb Logic Embedded High-K Charge Trap Transistor-Based Multi-Time-Programmable Memory With No Added Process Complexity

Abstract: This paper describes the design and implementation of an 80-kb logic-embedded non-volatile multi-time programmable memory (MTPM) with no added process complexity. Charge trap transistors (CTTs) that exploit charge trapping and de-trapping behavior in high-K dielectric of 32-/22-nm Logic FETs are used as storage elements with logic-compatible programming voltages. A high-gain slew-sense amplifier (SA) is used to efficiently detect the threshold voltage difference ( $\Delta V_{\textrm {DIF}}$ ) between the true and complement FETs in the twin cell. Design-assist techniques including multi-step programming with over-write protection and block write algorithm are used to enhance the programming efficiency without causing a dielectric breakdown. High-temperature stress results show a projected data retention of 10 years at 125 °C with a signal loss of <30% that is margined in while programming, by employing a sense margining logic in the SA. Scalability of CTT has been established by the first demonstration of CTT-based MTPM in 14-nm bulk FinFET technology with read cycle time of 40 ns at 0.7-V VDD.

Charge-Trap Transistors for CMOS-Only Analog Memory

Abstract: Since our demonstration of unsupervised learning using the CMOS-only charge-trap transistors (CTTs) as analog synapses, there has been an increasing interest in exploiting the device for various other neural network (NN) applications. However, most of these studies are limited to mere simulation due to the absence of detailed experimental device characterization. In this article, we provide a comprehensive investigation of the programming behavior of CTTs, including analog retention, intra- and inter-device variation, and fine-tuning of the device, both for individual devices and for devices in an integrated array. It is found that, after programming, the channel current gradually increases to a higher level, and the shift is larger when the device is programmed to a higher threshold voltage. With this postprogramming current increase appropriately accounted for, individual devices can be programmed to an equivalent precision of five bits, and three bits can be achieved for devices in an array. Our results reveal the promising future of using the CTT as a CMOS-only analog memory device.