On the reliability estimation of nano-circuits using neural networks

详细信息    查看全文

• 作者：Azam Beg^aAuthor Vitae ; Falah Awwad ; ^a ; ^{f_awwad@uaeu.ac.ae" class="auth_mail" title="E-mail the corresponding author} ; ^{fawwad62@yahoo.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Walid Ibrahim^aAuthor Vitae ; Faheem Ahmed^bAuthor Vitae
• 关键词：Reliability ; Modeling ; Neural network ; Digital circuit ; Nano-electronics
• 刊名：Microprocessors and Microsystems
• 出版年：2015
• 出版时间：November 2015
• 年：2015
• 卷：39
• 期：8
• 页码：674-685
• 全文大小：1185 K

文摘

As the integrated circuit geometries shrink, it becomes important for the designers to take into consideration the reliability of the circuits. Different techniques can be used for reliability calculation or estimation. Some of these techniques are accurate but time-consuming while others are quick but not accurate. For example, using a set of mathematical equations for reliability estimation is very fast but not precise enough for large systems. Alternatively, Monte Carlo simulations are highly accurate, but very time-intensive. This work presents three different neural network models for estimating circuit reliability. The models provide better prediction accuracies than the mathematical technique. A reasonably large number of combinational circuits were simulated over a wide range of device reliabilities to collect the training data for the models. Multiple slices of an ISCAS-85 benchmark circuit were used to validate the models’ prediction results.