Jiawei Cao;Chongtao Guo;Houjun Wang;Zhigang Wang;Hao Li;Geoffrey Ye Li
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引用次数: 0
Abstract
In this brief, we propose a deep learning-based performance testing framework to minimize the number of required test modules while guaranteeing the accuracy requirement, where a test module corresponds to a combination of one circuit and one stimulus. First, we apply a deep neural network (DNN) to establish the mapping from the response of the circuit under test (CUT) in each module to all specifications to be tested. Then, the required test modules are selected by solving a 0–1 integer programming problem. Finally, the predictions from the selected test modules are combined by a DNN to form the specification estimations. The simulation results validate the proposed approach in terms of testing accuracy and cost.
期刊介绍:
The IEEE Transactions on VLSI Systems is published as a monthly journal under the co-sponsorship of the IEEE Circuits and Systems Society, the IEEE Computer Society, and the IEEE Solid-State Circuits Society.
Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels.
To address this critical area through a common forum, the IEEE Transactions on VLSI Systems have been founded. The editorial board, consisting of international experts, invites original papers which emphasize and merit the novel systems integration aspects of microelectronic systems including interactions among systems design and partitioning, logic and memory design, digital and analog circuit design, layout synthesis, CAD tools, chips and wafer fabrication, testing and packaging, and systems level qualification. Thus, the coverage of these Transactions will focus on VLSI/ULSI microelectronic systems integration.