Bioprinting is a rapidly developing technique for biofabrication. Because of its high resolution and the ability to print living cells, bioprinting has been widely used in artificial tissue and organ generation as well as microscale living cell deposition. In this paper, we present a low-cost stereolithography-based bioprinting system that uses visible light crosslinkable bioinks. This low-cost stereolithography system was built around a commercial projector with a simple water filter to prevent harmful infrared radiation from the projector. The visible light crosslinking was achieved by using a mixture of polyethylene glycol diacrylate (PEGDA) and gelatin methacrylate (GelMA) hydrogel with eosin Y based photoinitiator. Three different concentrations of hydrogel mixtures (10% PEG, 5% PEG + 5% GelMA, and 2.5% PEG + 7.5% GelMA, all w/v) were studied with the presented systems. The mechanical properties and microstructure of the developed bioink were measured and discussed in detail. Several cell-free hydrogel patterns were generated to demonstrate the resolution of the solution. Experimental results with NIH 3T3 fibroblast cells show that this system can produce a highly vertical 3D structure with 50 μm resolution and 85% cell viability for at least five days. The developed system provides a low-cost visible light stereolithography solution and has the potential to be widely used in tissue engineering and bioengineering for microscale cell patterning.

Abstract-Efforts to achieve the long-standing dream of realizing scalable learning algorithms for networks of spiking neurons in silicon have been hampered by (a) the limited scalability of analog neuron circuits; (b) the enormous area overhead of learning circuits, which grows with the number of synapses; and (c) the need to implement all inter-neuron communication via off-chip address-events. In this work, a new architecture is proposed to overcome these challenges by combining innovations in computation, memory, and communication, respectively, to leverage (a) robust digital neuron circuits; (b) novel transposable SRAM arrays that share learning circuits, which grow only with the number of neurons; and (c) crossbar fan-out for efficient on-chip inter-neuron communication. Through tight integration of memory (synapses) and computation (neurons), a highly configurable chip comprising 256 neurons and 64K binary synapses with on-chip learning based on spike-timing dependent plasticity is demonstrated in 45nm SOI-CMOS. Near-threshold, event-driven operation at 0.53V is demonstrated to maximize power efficiency for real-time pattern classification, recognition, and associative memory tasks. Future scalable systems built from the foundation provided by this work will open up possibilities for ubiquitous ultra-dense, ultra-low power brain-like cognitive computers.

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