mpw-006 / VIPDM Pad Via

the VIPDM shapes of all of mpw-006

40 of 40 dies rendered.

31,866,037 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (17.0 MB in total, largest 657.2 kB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-006_VIPDM_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-006_VIPDM_reticle.png
the stitched reticle, full resolution
12.4 MB 00403ed45389b52c92937e17f46047a4cbd3b8bb9b76ebea461d98c563ae14804c880d21 d98ed3850bd8d588d2dcdb37d560f7c1c8da3d2301b2608906a405034f853881
mpw-006_VIPDM_reticle_overview.png
the same image scaled to fit 4096 px
517.1 kB 3ee545bed8d6f8e6164791260a39ad346d4645cb0e5a8101e388d5cde9579ff0ccaa08bc 8bd291325ce9865b157fe5912fe9714b7c9a96f97cbfec42c6a8c166c41ddff5
mpw-006_VIPDM_tiles.json
where every tile belongs, and its checksum
13.7 kB 810555ef9f42e10fe7ba145612c6b1586c8fabe4c83f80d745102d013e8c4a168742ded4 54dbb89445f90f1359f70a184a35e5e31fdffefaaeb26343bbed4c46c17d4277
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 free_and_open_source_silicon_implementation_of_aes 7576×107760, 0 366.8 kB7a5717d66bbca6fde737e58f40cdcb3b3b21e6bd424d19073daf699256d2604e
A2 microwatt_mpw6 7576×107767576, 0 584.9 kB8a5ce577d2a0e14f820d1316e183d5d4b22ff8fa03921b5944243cef8bfa914c
A3 hack_soc_-_mpw-6 7576×1077615152, 0 346.8 kB4fc14766a7d25105dbea9fc36fe6f2f2fe92cbd883c6c64aacf12c5a2c6ba7d9
A4 riscduino-dcore_d3_ 7576×1077622728, 0 560.8 kB9f08a69111212294bdc7105e487caf15b7b62847d6349cf4372bcb831270ff51
A5 riscduino-qcore_q2_ 7576×1077630304, 0 657.2 kB54f593aa7bba5c407287afb951837ff267fc010b7dda3bb110c1075965d1cf4d
A6 riscduino-score-s4-2 7576×1077637880, 0 510.5 kB7d32214ebb6a86f632ebf0d85ec5e8fde4610d91bc699c56784e0aba736480cf
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 sonaronchip8 7576×107760, 10776 469.0 kB14ffcf6198f303e76755dc56828c470587264093dab8284c71df48ae900ad1fb
B2 ft8_receiver_test 7576×107767576, 10776 330.3 kBb1278476ce9fd5630f9d902d4b2b12c3eeea4821ab9d2a9449fc199201bbd33d
B3 azadi_iii_dft 7576×1077615152, 10776 595.5 kB8c1fe2c172c2c178712fa133b6ffaf86dbe6359fa7c1f01be860426e67394626
B4 sramtestvehiclesequel 7576×1077622728, 10776 325.0 kBf7ff19be14fa3f7e68f034916510b11e20e8779e91b4b242530d20f91ab80377
B5 isa_16_bit_microprocessor 7576×1077630304, 10776 396.3 kB16bcc8c74fe6fdd0df1cfae85e85049775bebfd0ad6e41ac97ba1cf23e3004b7
B6 asicle-ro 7576×1077637880, 10776 477.9 kBd527c22c500f69e12ed718a6c588578fc422c3d974161fee55cd43ffb31e2bc9
B7 10b_adc_and_analog_support 7976×1077645456, 10776 342.7 kB00bfb21929c7475d2b219afd70614fc9199703703339b106012b084b5af7fd69
C1 yonga-modbus_controller 7576×107760, 21552 367.7 kBc519cb203d861a2f8eada79fd7608b0f1b53a82e189f4126c735a6e5dbf96a09
C2 christmastreecontroller__mpw5_rerun_ 7576×107767576, 21552 379.5 kBe6ca4c2b7ba5ccf09929bd365ef0eecbdded1c11ad0efb97637d76886e142760
C3 openram_test_design 7576×1077615152, 21552 462.8 kB8e26e2049dcf16a0c5ed67485ce98bd4a4d5d659828d5bbbeeab48ae7a5ff1d1
C4 pwm__resubmission_from_mpw-5_ 7576×1077622728, 21552 368.7 kBa853c6b1af0c5ee21a10ed2273da0f54ca22d8f6daed1946904f0039f25048cd
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 hyperram_interface 7576×1077637880, 21552 367.0 kB3c8eef2b7e8aa660dc9c22eebc1dfaafca45eebb277f84cee060b9a014d07139
C7 adaptive_cordic-based_32-point_dct 7976×1077645456, 21552 379.7 kB0c1c80e8d758e6d7ff6bd7b57c9c72789dec137638447ff91f3caf6823d6563c
D1 cmos_cascode_dynamic_comparator 7576×107760, 32328 326.6 kB4b5556a580e1582e34f19df37b7aa981c066299d4ca142dcf376c3826e9cb7ef
D2 cmos_high_speed_dynamic_comparator 7576×107767576, 32328 326.3 kBee67300d96593a3f7682e30620d6507164293db3341ad8b0bb7bdac573f1def6
D3 order_prga_mpw6 7576×1077615152, 32328 472.4 kB2b6c61c847a4316630fa47b2d727778a617b70385d959e085cbf3b62f9b42293
D4 temporal_runtime_monitoring_component 7576×1077622728, 32328 376.4 kB908384769d3be14af0336f390682ef92d649252ee04f0534f08f2ec5dc8fb19e
D5 large_memory_micro 7576×1077630304, 32328 558.7 kBf67de7d0961fb2e1ab7042f9c93106dc9e954fff25c3a282cf98538881abd603
D6 rng_chaos_scroll 7576×1077637880, 32328 372.4 kBae090f492f4113d80259f88e09964d77f8a7ac6ffd73bed0abed330204c4cbb5
D7 mixed_signal_circuits_mpw6 7976×1077645456, 32328 331.9 kB212d74b14ee918a7b3d7806afa588d726ef8edfc24778a69da2cbb86e72c8335
E1 mixed_signal_circuits_v2 7576×107760, 43104 325.0 kBf7ff19be14fa3f7e68f034916510b11e20e8779e91b4b242530d20f91ab80377
E2 direct_conversion_receiver_mpw6 7576×107767576, 43104 325.9 kB1939188253de0a1cf937b66b882a19961cf9a32cba97866cd01100db439a3946
E3 floating_point_halfprecision 7576×1077615152, 43104 378.6 kBe7ebecb5701748f23e09b1bfa3cb01575a2db496a7e4236f9f3128fdf33870b6
E4 floating_point_unit_bfloat16 7576×1077622728, 43104 378.0 kBf1319f6f20f475dae10b93b42ff74781643d4b2e8403123cdb3a1292f8ffc5fe
E5 floating_point_unit_single_precision 7576×1077630304, 43104 379.0 kBdaad7056dc144ff8128a63074d419df0a6a95a0f2f5fbe700bdd150bf9d63f38
E6 experiar_soc 7576×1077637880, 43104 565.1 kB8b0bdc9aafc14568364acbe0edc8c184856f64e182cbf1d24925d065df021f1b
E7 my_caravel_analog_project 7976×1077645456, 43104 334.9 kBc62d1a70712afb0ac49bad6a0a991b548b3bd2b959fd108a440953039bf2d6b3
F1 efabless_mpw6_riscduino 7576×111760, 53880 517.8 kB5e5e78b1a67478fa928ff159817770c903f6223dfee6d33538532ca8e66438e7
F2 motius_pong 7576×111767576, 53880 364.4 kBc3f42be2276bdfab297b813e59e3b688f175c3065a10713aa036ffe8bbe43b61
F3 soc_now 7576×1117615152, 53880 392.9 kB75a9f36925b666b241ea31deb24ff04a2f0cfaf5c73d1b0336046a283c2c220a
F4 ORDER_PRGA_MPW6_v2 7576×1117622728, 53880 476.4 kB6fff4c4bedb83deddb3299b6f2ed188e8e05e0f35a5e9b4f72f5e2910cca73b0
F5 optimised_strong_arm_latch 7576×1117630304, 53880 330.3 kB4a8d432747ae945e9cd5adeb98294e59dd43641de5037083ab06ddf3cd521033
F6 riscduino_qcore_folk 7576×1117637880, 53880 644.1 kB93cbfd90145ac2c36a4ef72d266c6603ca8d82a905b986c5645fdcd2dc5fe1a4
F7 tensor_calculation_using_nngen 7976×1117645456, 53880 362.8 kB57ab8bad5ce071d65377bc16b08f1ee7980670b5cb9dda7f559c54478a419b9f

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About VIPDM on mpw-006

Resolved: reticle 5CS8014AC, run mpw-006, mask VIPDM

Mask layer
VIPDM — Pad Via
Drawn layer
70:44
Detail
VIPDM = via3 (pad-via flow, not SKY130)
Used in SKY130
no

mpw-006 — SKY130 MPW-6

Foundry product
EF MPW#6 (+ 'With ReRAM' split lot)
Reticle set
5CS8014AC
Run date
2022-04-11
Submissions closed
2022-06-08
Projects
85
Fab lot
Identification
confirmed — Lot records: EF MPW#6 ('Held pre-mask' in late 2022; MPW-6 taped out 2022-12)
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-6 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-006/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.