Instigated by the plethora of data generated by edge devices and IoT devices, machine learning has become the de facto choice of everyone for solving many tasks. The applications like intelligent healthcare monitoring systems, smartwatches, or automatic cars require real-time processing of the data or image, which is done by machine learning algorithms with higher efficiency than humans. There are two possible methods for artificial intelligence.
1. non-Von-Neumann hardware-based implementation of neural networks.
2. traditional computer science base approach for neural networks or traditional Von-Neumann architecture-based implementation of neural networks.
The standard Von-Neumann performance of neural networks, where the memory and the computation parts are segregated, severely suffers from latency with the rising number of edge devices. However, the plethora of usage of edge devices in our daily life foists stringent restrictions on latency, device area, and power consumption for the hard wares. Therefore, we need to take the route beyond CMOS-based mixed-signal implementation of neural networks, where the memory bandwidth is not limited by quintessential Von-Neumann architecture. Recent signs of progress in the research of emerging non-volatile memories have fueled the idea of non-Von-Neumann computing.
This work primarily focuses on increasing the reliability of ferroelectric finFET devices for neuromorphic computing applications. We have adopted a three-way optimization process from semiconductor process, device bias, and system-level optimization. Optimization of semiconductor process led to 3bit/cell operation along with improvement in device endurance. We have further demonstrated that bias optimization combined with process optimization can lead to robust binary neural network operation from -40◦C to 125◦C using Fe-finFET devices as the synapse.
The overall performance of a neuromorphic system results from the confederated performance of the device, peripheral circuits, network architecture, and algorithm. This thesis focuses on optimizing the Fe-finFET based neural network system from all three perspectives, and we conclude that the systematic variation in Fe-finFETs has a more substantial impact than random variation.

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