GLSVLSI

In this paper we present the first Decision Diagrams (DDs) based methodology for verifying the Resistive Random Access Memory (ReRAM) synthesis process. In particular, we propose a methodology which leverages Binary Decision Diagrams (BDDs), ...

Continuous-Flow Microfluidic Biochips (CFMBs) have been widely applied in various biochemical fields due to their capabilities of precise control, high integration, and automation. However, insecure supply chains enable malicious actors to tamper with ...

Due to the lengthy design cycle, generating legal, diverse and valid layout patterns artificially to expand VLSI layout pattern libraries has become an important problem to solve in order to facilitate modern design-for-manufacturability (DFM) studies. ...

With the advances in semiconductor technology, the sizes of transistors are getting smaller, which has led to an increasingly severe impact of IR drop. Consequently, this trend has amplified the significance of IR drop analysis within the realm of chip ...

With the continuous shrinking of feature size, directed self-assembly (DSA) has gradually become one of the leading candidates for extending the resolution of optical lithography to sub-7 nm and beyond, a DSA-based extra via (EV) insertion scheme is the ...

Placement and routing (P&R) are two crucial stages in the physical design process to optimize different objectives. For instance, placement often focuses on optimizing the half-perimeter wirelength (HPWL) and estimated congestion while routing attempts ...

In recent years, Fully Programmable Valve Array (FPVA) has emerged as a promising alternative for microfluidic biochips with flexible features. When two fluids flow sequentially through the same microchannel, the latter will be contaminated by the ...

Due to manufacturing defects, chip aging, and potential malicious attacks, unexpected errors may occur in the valves of Fully Programmable Valve Array (FPVA) biochips. To address this issue, an error recovery method based on Deep Reinforcement Learning (...

Quantization is vital for deploying neural networks on Computing-In-Memory (CIM) based accelerators due to inherent limitations in memory devices and data interfaces’ representational capacities. However, traditional quantization algorithms often ...