Chapter 4. High Temperature Barrier Layers
The concept of hybrid electrode-barrier layers is introduced in the first section. The experimental process and ferroelectric properties of PZT films on the electrode-barrier layers are explained in detail in the second section. The fatigue problem and minimization of the fatigue are reviewed, and the improvement of the fatigue properties using the high temperature multi-layer substrates are discussed in the last section. 4.1 INTRODUCTION In the previous two chapters, high density destructive readout (DRO) FRAM were investigated by primarily focusing on materials processing temperature. The low temperature processing was successfully achieved for the high density DRO FRAM applications by using a modified sol-gel processing. The other approach we have taken for integrating PZT films into high density CMOS devices is to develop novel electrode-barrier layers for high temperature processing. Since the currently used diffusion barrier layers such as TiN and TiSi 2 maintain their electrical conductivity only up to 550 °C, high temperature barrier layers are desirable for the integration of PZT films into high density CMOS devices. Ideal electrode materials for the DRO FRAM devices should possesses good electrical conductivity, outstanding thermal and chemical stability, and amenability to dry etching. In particular, the electrode-barrier should be a good diffusion barrier without reacting the ferroelectric materials or the underlying substrate (poly-Si) during high temperature processing. In this research, we propose Pt/IrO 2 /Ir hybrid electrode

 Report

ra n g e d f ro m 3 0 t o 1 9 0 m g /L .  C h a n n e l d iv e rs io n  N o m a jo r c h a n g e in o v e ra ll c a tc h m e n t a n d th u s a d v e rs e im p a c t to h y d ro lo g y a n d th e a s s o c ia te d w a te r q u a lit y im p a c t is n o t a n ti c ip a te d .  S e w e ra g e p ro p o s e d b y t h e d e v e lo p m e n t c o u ld b e b e n e fi c ia l to e x is ti n g w a te r q u a lit y.
Responsibility, Autonomy and Accountability: Legal Liability for Machine Learning
This paper investigates the question of legal liability for the consequences of decisions made by machine learning technology rather than by humans, although we do not attempt a detailed analysis of the basis on which such liability might be imposed. This is a substantial task which would require far more space than is available here.The initial focus is on private claims for personal injury, property damage and other losses caused by use of machine learning technologies. These claims will usually be made via the tort of negligence.Equally importantly, we identify some of the threats to individual autonomy and fundamental rights which are created by the use of machine learning to make decisions. Breach of those fundamental rights is a second source of potential liability.We conclude by suggesting a potential link between liability and the preservation of those fundamental rights which might achieve an interim solution to this issue, making use of the concept of accountability and its transparency attribute in particular.