Bacterial surface fouling is problematic for a wide range of applications and industries, including, but not limited to medical devices (implants, replacement joints, stents, pacemakers), municipal infrastructure (pipes, wastewater treatment), food production (food processing surfaces, processing equipment), and transportation (ship hulls, aircraft fuel tanks). One method to combat bacterial biofouling is to modify the topographical structure of the surface in question, thereby limiting the ability of individual cells to attach to the surface, colonize, and form biofilms. Multiple research groups have demonstrated that micro and nanoscale topographies significantly reduce bacterial biofouling, for both individual cells and bacterial biofilms. Antifouling strategies that utilize engineered topographical surface features with well-defined dimensions and shapes have demonstrated a greater degree of controllable inhibition over initial cell attachment, in comparison to undefined, texturized, or porous surfaces. This review article will explore the various approaches and techniques used by researches, including work from our own group, and the underlying physical properties of these highly structured, engineered micro/nanoscale topographies that significantly impact bacterial surface attachment.

It is well documented that bacterial adhesion to surfaces is mediated by the physical and chemical properties of the substrate, as well as the surface characteristics of the organism. Topographical features that limit cell–surface interactions have been shown to reduce surface colonization and biofilm formation. In this study, bacterial attachment to medically relevant materials was evaluated. Our data show that Escherichia coli attachment to glass, silicone, and titanium surfaces was most affected by the surface energy of these materials, as determined by water contact angle. The inherent roughness of the surface, however, was not correlated with cell attachment density. To study the effect of engineered surface roughness on bacterial attachment, topographical features, including arrays of holes and repeating lines/trenches, were formed from silicon wafers and then used as a template to imprint silicone-based polydimethylsiloxane (PDMS). Patterned silicone surfaces were then used in static and microfluidic flow-based experiments to evaluate cellular settlement and attachment. Cell attachment was observed to be strongly dependent upon the topographical features under both static and microfluidic flow conditions. The highest attachment density was observed on flat, un-patterned surfaces, while linear patterned surfaces showed greatly reduced cell attachment. Moreover, surfaces consisting of arrays of holes further reduced cell attachment as compared to linear patterns. These results demonstrate that the size, spacing, and shape of surface features play a significant role in cell–surface attachment and provide insight for the design of surfaces with antifouling properties.

Mutli-level switching in resistive memory devices enables a wide range of computational paradigms, including neuromorphic and cognitive computing. To this end, we have developed a bi-layer tantalum oxide based resistive random access memory device using Hf as the oxygen exchange layer. Multiple, discrete resistance levels were achieved by modulating the RESET pulse width and height, ranging from 2 kΩ to several MΩ. For a fixed pulse height, OFF state resistance was found to increase gradually with the increase in the pulse width, whereas for a fixed pulse width, the increase in the pulse height resulted in drastic changes in resistance. Resistive switching in these devices transitioned from Schottky emission in the OFF state to tunneling based conduction in the ON state, based on I-V curve fitting and temperature dependent current measurements. These devices also demonstrated endurance of more than 10⁸ cycles with a satisfactory R_off/R_on ratio and retention greater than 10⁴ s.

RRAM-based in-memory computing (IMC) effectively accelerates deep neural networks (DNNs) and other machine learning algorithms. On the other hand, in the presence of RRAM device variations and lower precision, the mapping of DNNs to RRAM-based IMC suffers from severe accuracy loss. In this work, we propose a novel hybrid IMC architecture that integrates an RRAM-based IMC macro with a digital SRAM macro using a programmable shifter to compensate for the RRAM variations and recover the accuracy. The digital SRAM macro consists of a small SRAM memory array and an array of multiply-and-accumulate (MAC) units. The nonideal output from the RRAM macro, due to device and circuit nonidealities, is compensated by adding the precise output from the SRAM macro. In addition, the programmable shifter allows for different scales of compensation by shifting the SRAM macro output relative to the RRAM macro output. On the algorithm side, we develop a framework for the training of DNNs to support the hybrid IMC architecture through ensemble learning. The proposed framework performs quantization (weights and activations), pruning, RRAM IMC-aware training, and employs ensemble learning through different compensation scales by utilizing the programmable shifter. Finally, we design a silicon prototype of the proposed hybrid IMC architecture in the 65-nm SUNY process to demonstrate its efficacy. Experimental evaluation of the hybrid IMC architecture shows that the SRAM compensation allows for a realistic IMC architecture with multilevel RRAM cells (MLCs) even though they suffer from high variations. The hybrid IMC architecture achieves up to 21.9%, 12.65%, and 6.52% improvement in post-mapping accuracy over state-of-the-art techniques, at minimal overhead, for ResNet-20 on CIFAR-10, VGG-16 on CIFAR-10, and ResNet-18 on ImageNet, respectively.